├── README.md ├── __init__.py ├── generate_eval_dataset.py ├── generate_train_dataset.py ├── graph2text.py ├── metadata ├── OntologyClasses.xml ├── delex.txt ├── entities.list ├── entity_type.list ├── onto_classes_system.json ├── ontology_ponderated_occurences.json ├── prop_schema.list └── properties.list ├── multi-bleu.perl ├── relex_predictions.py ├── support ├── __init__.py ├── generate_eval_metadata.py ├── generate_lists.py ├── generate_nodelex.py ├── generate_onto_classes_system.py ├── get_entity_type.py ├── get_property_schema.py └── link_metadata.py └── utils ├── __init__.py ├── rdf_utils.py ├── sparql_utils.py └── text_utils.py /README.md: -------------------------------------------------------------------------------- 1 | # Learning to generate text from RDF fact graphs with seq2seq models 2 | 3 | #### Project work by Badr Abdullah, François Buet, and Reem Mathbout. 4 | 5 | The repository contains code to prepare data for training models that generate natural language text from RDF triples using the WebNLG data (more info about the data can be found in this paper: http://webnlg.loria.fr/pages/webnlg-challenge-report.pdf). All modules in this project have been developed 6 | and tested with python 3.6. There are only two non-core Python libraries required to run the modules: NLTK and SPARQLWrapper. 7 | 8 | For now, the code can be used to parse RDF data from XML files, apply text preprocessing, retrieve semantic types for RDF entities, perform delexicalization on the target sentences and generate datasets to be used for 9 | training and evaluations sequence to sequence models for NLG from fact graphs. 10 | 11 | ## Generating Training Datasets 12 | To use the module for generating training datasets, use: 13 | 14 | ###### Flat sequences in the source side 15 | ``` 16 | mkdir ../datasets 17 | python generate_train_dataset.py \ 18 | -path ../challenge_data_train_dev/train \ 19 | -src_mode flat \ 20 | -src ../datasets/train.src \ 21 | -tgt ../datasets/train.tgt 22 | ``` 23 | 24 | Example: the pair {*triple:* Albany , Oregon | country | United States, *text:* "Albany , Oregon is in the U.S."} would be represented as follows: 25 | ``` 26 | src: ENTITY_1 CITY country ENTITY_2 COUNTRY 27 | 28 | tgt: ENTITY_1 is in the ENTITY_2 . 29 | ``` 30 | 31 | 32 | ###### Structured sequences in the source side 33 | ``` 34 | python generate_train_dataset.py \ 35 | -path ../challenge_data_train_dev/train \ 36 | -src_mode structured \ 37 | -src ../datasets/train.src \ 38 | -tgt ../datasets/train.tgt 39 | ``` 40 | 41 | Example: the pair (triple: Albany , Oregon | country | United States, "Albany , Oregon is in the U.S.") would be represented as follows: 42 | ``` 43 | src: ( ( ENTITY_1 CITY ( country ( ENTITY_2 COUNTRY ) ) ) ) 44 | 45 | tgt: ENTITY_1 is in the ENTITY_2 . 46 | ``` 47 | 48 | ## Generating Evaluation Datasets 49 | To use the module for generating evaluation (dev and test) datasets, use: 50 | 51 | (Running this script would generate 5 files: dev.src, dev.tgt, dev.ref1, dev.ref2, dev.ref3, and dev.relex) 52 | 53 | ###### Flat sequences in the source side 54 | ``` 55 | mkdir ../datasets 56 | python generate_eval_dataset.py \ 57 | -path ../challenge_data_train_dev/dev \ 58 | -src_mode flat \ 59 | -src ../datasets/dev.src \ 60 | -tgt ../datasets/dev.tgt \ 61 | -ref ../datasets/dev.ref \ 62 | -relex ../datasets/dev.relex 63 | ``` 64 | 65 | ###### Structured sequences in the source side 66 | ``` 67 | python generate_eval_dataset.py \ 68 | -path ../challenge_data_train_dev/dev \ 69 | -src_mode structured \ 70 | -src ../datasets/dev.src \ 71 | -tgt ../datasets/dev.tgt \ 72 | -ref ../datasets/dev.ref \ 73 | -relex ../datasets/dev.relex 74 | ``` 75 | 76 | ## Using OpenNTM 77 | Once we have the data, we can train a seq2seq model. We show how to use OpenNMT for this task: 78 | (probably you would want to move the datasets directory to where you have installed OpenNMT) 79 | 80 | ### 1. Preprocess the data. 81 | 82 | ``` 83 | th preprocess.lua \ 84 | -train_src datasets/train.src \ 85 | -train_tgt datasets/train.tgt \ 86 | -valid_src datasets/dev.src \ 87 | -valid_tgt datasets/dev.tgt \ 88 | -save_data datasets/data_tensor 89 | ``` 90 | 91 | ### 2. Train a model. 92 | ``` 93 | th train.lua -data datasets/data_tensor-train.t7 -save_model s2s_model 94 | ``` 95 | 96 | ### 3. Use the model for inference. 97 | ``` 98 | th translate.lua -model s2s_model_epochX_PPL.t7 -src datasets/dev.src -output predictions.dev 99 | ``` 100 | 101 | ### 4. Relexicalize predictions. 102 | ``` 103 | python relex_preditions.py \ 104 | -pred predictions.dev \ 105 | -relex datasets/dev.relex \ 106 | -output predictions.relex 107 | ``` 108 | 109 | ### 5. Evaluate with BLEU script. 110 | ``` 111 | multi-bleu.perl datasets/dev.ref < predictions.relex 112 | ``` 113 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/badrex/rdf2text/bdc72db893d76972af196123b71ef0c6b2d3e552/__init__.py -------------------------------------------------------------------------------- /generate_eval_dataset.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to process RDF data and generate dataset for evaluation and testing. 3 | """ 4 | 5 | from utils import rdf_utils 6 | import argparse 7 | from graph2text import * 8 | 9 | def generate(): 10 | """ 11 | Read options from user input, and generate eval dataset. 12 | """ 13 | 14 | DISCR = 'Generate dataset from XML files of RDF to Text Entries.' 15 | parser = argparse.ArgumentParser(description=DISCR) 16 | parser.add_argument('-path', type=str, help='Path to data.', required=True) 17 | parser.add_argument('-src_mode', help='Source mode: flat or structured.', 18 | choices=['flat', 'structured'], default = 'flat', nargs = '?') 19 | parser.add_argument('-delex_mode', type=str, help='Advanced or simple delexicalization', 20 | choices=['simple', 'adv'], default = 'adv', nargs = '?') 21 | 22 | parser.add_argument('-src', type=str, help='Path to output file for src.', 23 | required=True) 24 | parser.add_argument('-tgt', type=str, help='Path to output file for tgt.', 25 | required=True) 26 | parser.add_argument('-ref', type=str, help='Path to output ref files.', 27 | required=True) 28 | parser.add_argument('-relex', type=str, help='Path to ouput relex file.', 29 | required=True) 30 | 31 | 32 | args = parser.parse_args() 33 | 34 | instances, _, _ = rdf_utils.generate_instances(args.path, eval=True) 35 | 36 | for (size, ins) in instances.items(): 37 | for i in ins: 38 | lexs = i.Lexicalisation 39 | 40 | for k in range(len(lexs), 3): 41 | lexs.append(rdf_utils.Lexicalisation('', '', '')) 42 | 43 | tripleset = (i.originaltripleset, i.modifiedtripleset) 44 | G = FactGraph(tripleset, lexs[0].lex) 45 | 46 | print(G.lexicalization) 47 | 48 | # get relexicalization dict 49 | relex_entities = [e for (id, e) in sorted(G.id2entity.items())] 50 | 51 | assert len(relex_entities) == len(G.entities) , \ 52 | "The number of entities and size of relexicalization dict do not match." 53 | 54 | # write relex_dict to a file 55 | with open(args.relex, 'a+', encoding="utf8") as relexFile: 56 | relexFile.write('\t'.join(relex_entities) + '\n') 57 | 58 | # write src 59 | with open(args.src, 'a+', encoding="utf8") as srcFile: 60 | if args.src_mode == 'structured': 61 | srcFile.write(G.linearize_graph(structured=True, incoming_edges=True).replace('\n', ' ') + '\n') 62 | else: 63 | srcFile.write(G.linearize_graph() + '\n') 64 | 65 | # write tgt 66 | adv_delex = False if args.delex_mode == 'simple' else True 67 | 68 | with open(args.tgt, 'a+', encoding="utf8") as tgtFile: 69 | tgtFile.write(G.delexicalize_text(adv_delex).replace('\n', ' ') + '\n') 70 | 71 | # write ref 72 | for i in range(3): 73 | ref_str = text_utils.tokenize_and_concat(lexs[i].lex) 74 | with open(args.ref + str(i + 1), 'a+', encoding="utf8") as refFile: 75 | refFile.write(ref_str.replace('\n', ' ') + '\n') 76 | 77 | 78 | def main(): 79 | generate() 80 | 81 | if __name__ == '__main__': 82 | main() 83 | -------------------------------------------------------------------------------- /generate_train_dataset.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to process RDF data and generate dataset for training. 3 | """ 4 | 5 | from utils import rdf_utils 6 | import argparse 7 | from graph2text import * 8 | 9 | def generate(): 10 | """ 11 | Read options from user input, and generate dataset. 12 | """ 13 | 14 | DISCR = 'Generate dataset from XML files of RDF to Text Entries.' 15 | parser = argparse.ArgumentParser(description=DISCR) 16 | parser.add_argument('-path', type=str, help='Path to data.', required=True) 17 | parser.add_argument('-src_mode', help='source mode: flat or structured.', 18 | choices=['flat', 'structured'], default = 'flat', nargs = '?') 19 | 20 | parser.add_argument('-delex_mode', type=str, help='Advanced or simple delexicalization', 21 | choices=['simple', 'adv'], default = 'adv', nargs = '?') 22 | 23 | parser.add_argument('-src', type=str, help='Path to output file for src.', 24 | required=True) 25 | parser.add_argument('-tgt', type=str, help='Path to output file for tgt.', 26 | required=True) 27 | 28 | args = parser.parse_args() 29 | 30 | instances, _, _ = rdf_utils.generate_instances(args.path) 31 | 32 | for (size, ins) in instances.items(): 33 | for i in ins: 34 | tripleset = (i.originaltripleset, i.modifiedtripleset) 35 | G = FactGraph(tripleset, i.Lexicalisation.lex) 36 | 37 | with open(args.src, 'a+', encoding="utf8") as srcFile: 38 | if args.src_mode == 'structured': 39 | srcFile.write(G.linearize_graph(structured=True, incoming_edges=True) + '\n') 40 | else: 41 | srcFile.write(G.linearize_graph() + '\n') 42 | 43 | # write tgt 44 | adv_delex = False if args.delex_mode == 'simple' else True 45 | 46 | with open(args.tgt, 'a+', encoding="utf8") as tgtFile: 47 | tgtFile.write(G.delexicalize_text(adv_delex).replace('\n', ' ') + '\n') 48 | 49 | 50 | def main(): 51 | generate() 52 | 53 | if __name__ == '__main__': 54 | main() 55 | -------------------------------------------------------------------------------- /graph2text.py: -------------------------------------------------------------------------------- 1 | """ 2 | A module for RDF Entity, RDF Property, and FactGraph. 3 | """ 4 | 5 | from utils import text_utils, rdf_utils, sparql_utils 6 | import xml.etree.ElementTree as et 7 | import re 8 | import string 9 | from collections import defaultdict 10 | 11 | 12 | # populate a dict for schema properties: prop --> (domain, range) 13 | prop2schema = {} 14 | 15 | with open('metadata/prop_schema.list', encoding="utf8") as f: 16 | for line in f.readlines(): 17 | (p, d, r) = line.strip().split() 18 | prop2schema[p] = (d, r) 19 | 20 | # populate a dict for semantic types of entities: entity --> type 21 | entity2type = {} 22 | 23 | with open('metadata/entity_type.list', encoding="utf8") as f: 24 | for line in f.readlines(): 25 | last_space_idx = line.rfind(' ') 26 | entity = line[:last_space_idx] 27 | stype = line[last_space_idx:] 28 | 29 | hash_idx = stype.find('#') 30 | stype = stype[hash_idx + 1:] 31 | 32 | entity2type[entity] = stype.strip() 33 | 34 | 35 | class RDFEntity: 36 | """ A class to represent an RDF entity. """ 37 | 38 | def __init__(self, ID, o_rdf_entity, m_rdf_entity, semantic_type=None): 39 | """ 40 | Instantiate an entity. 41 | :param o_rdf_entity: original entity from an RDF triple (dtype: string) 42 | :param m_rdf_entity: modified entity from an RDF triple (dtype: string) 43 | """ 44 | self.ID = 'ENTITY_' + str(ID) 45 | 46 | # the join function is used to substitute multiple spaces with only one 47 | self.olex_form = ' '.join(self.text_split(o_rdf_entity).split()) 48 | self.mlex_form = ' '.join(self.text_split(m_rdf_entity).split()) 49 | 50 | if semantic_type is None: 51 | self.stype = entity2type[o_rdf_entity] 52 | else: 53 | hash_idx = semantic_type.find('#') 54 | semantic_type = semantic_type[hash_idx + 1:] 55 | self.stype = semantic_type 56 | 57 | self.aliases = self.get_aliases() 58 | 59 | def text_split(self, entity_str): 60 | """Return the text of the entity after split.""" 61 | return ' '.join(entity_str.split('_')) 62 | 63 | def get_aliases(self): 64 | """Return a list of aliases for the entity.""" 65 | # TODO: find a way to retrieve a list of aliases for an entity from 66 | # (for example) an online resource 67 | return [self.mlex_form, self.mlex_form.lower()] 68 | 69 | def set_stype(self, semantic_type): 70 | """Given a semantic_type, modify self.stype.""" 71 | self.stype = semantic_type 72 | 73 | 74 | class RDFProperty: 75 | """ A class to represent RDF property (predicate). """ 76 | 77 | def __init__(self, o_rdf_property, m_rdf_property): 78 | """ 79 | Instantiate a property. 80 | :param o_rdf_property: original prop from an RDF triple (dtype: string) 81 | :param o_rdf_property: modified prop from an RDF triple (dtype: string) 82 | """ 83 | self.olex_form = self.text_split(o_rdf_property) 84 | self.mlex_form = self.text_split(m_rdf_property) 85 | self.type_form = o_rdf_property.upper() 86 | self.domain = prop2schema[o_rdf_property][0] 87 | self.range = prop2schema[o_rdf_property][1] 88 | 89 | def text_split(self, property_string): 90 | """Return the text of the property after (camelCase) split.""" 91 | return text_utils.camel_case_split(property_string) 92 | 93 | 94 | class FactGraph: 95 | """ 96 | A class to represent RDF graph instances for natural language generation 97 | from structed input (e.g. RDF triples from a knowledge base). 98 | NOTE: Training instances are represented as (graph, text) pairs, while eval 99 | and test instances are represented only as graphs. 100 | """ 101 | 102 | def __init__(self, tripleset_tuple, lexicalization=None): 103 | """ 104 | Initialize and construct a graph from RDF triples. 105 | :param rdf_triples: a set of structured RDF triples (dtype: Tripleset) 106 | :param lexicalization: a text that realises the triple set for training 107 | instances 108 | :dtype: string 109 | :default: None (for eval and test instances). 110 | """ 111 | 112 | # a set of RDF triples 113 | otripleset, mtripleset = tripleset_tuple 114 | # original 115 | self.o_rdf_triples = otripleset[0].triples 116 | 117 | # modified 118 | self.m_rdf_triples = mtripleset.triples 119 | 120 | assert len(self.o_rdf_triples) == len(self.m_rdf_triples), \ 121 | "Original and modified tripleset are not the same length." 122 | 123 | # for training instances, initilize the corresponding lexicalization 124 | if lexicalization: 125 | self.lexicalization = lexicalization 126 | 127 | # a dict for entities in the graph instance 128 | # this dict maps from lex_form of entity to entity object 129 | self.entities = {} 130 | 131 | # id2entity dict is used for relexicalization for eval datasets 132 | self.id2entity = {} 133 | 134 | # a dict for properties in the graph instance 135 | # this dict maps from lex_form of property to property object 136 | self.properties = {} 137 | 138 | # call contruct_graph() method to build the graph from the RDF triples 139 | 140 | # two dicts to link entities in the graph instance (subj --> (prop, obj)) 141 | # and (obj --> (prop, subj)) 142 | # these data structures make easy to generate structured sequences 143 | self.subj2obj = {} 144 | self.obj2subj = {} 145 | 146 | # call method to construct entity graph and populate other dicts 147 | self._contruct_graph() 148 | 149 | def _contruct_graph(self): 150 | """ 151 | Build the graph. 152 | Populate entities, properties, subj2obj, and obj2subj dicts. 153 | """ 154 | entityID = 0 155 | 156 | # loop through each zipped triple 157 | for (otriple, mtriple) in zip(self.o_rdf_triples, self.m_rdf_triples): 158 | # extract nodes (entities) and edges (properties) from original 159 | o_subj = otriple.subject 160 | o_obj = otriple.object 161 | o_prop = otriple.property 162 | 163 | # extract nodes (entities) and edges (properties) from modified 164 | m_subj = mtriple.subject 165 | m_obj = mtriple.object 166 | m_prop = mtriple.property 167 | 168 | # update properties dict by instantiating RDFProperty objects 169 | if o_prop not in self.properties: 170 | self.properties[o_prop] = RDFProperty(o_prop, m_prop) 171 | 172 | # update entities dict by instantiating RDFEntity objects 173 | if o_subj not in self.entities: 174 | entityID += 1 175 | # first try to use the domain of the property 176 | if self.properties[o_prop].domain != '*': 177 | self.entities[o_subj] = RDFEntity(entityID, o_subj, m_subj, 178 | self.properties[o_prop].domain) 179 | 180 | # directly retrieve the type of the subj 181 | else: 182 | self.entities[o_subj] = RDFEntity(entityID, o_subj, m_subj) 183 | 184 | # add to id2entity dicr 185 | self.id2entity[entityID] = self.entities[o_subj].mlex_form 186 | 187 | if o_obj not in self.entities: 188 | entityID += 1 189 | # we first try to use the range of the property 190 | if self.properties[o_prop].range != '*': 191 | self.entities[o_obj] = RDFEntity(entityID, o_obj, m_obj, 192 | self.properties[o_prop].range) 193 | 194 | # try to directly retrieve the type of the obj 195 | else: 196 | self.entities[o_obj] = RDFEntity(entityID, o_obj, m_obj) 197 | 198 | # if the stype is 'THING', use the expression of the prop as a type 199 | if self.entities[o_obj].stype == 'THING': 200 | self.entities[o_obj].set_stype(self.properties[o_prop].type_form) 201 | 202 | # add to id2entity dicr 203 | self.id2entity[entityID] = self.entities[o_obj].mlex_form 204 | 205 | # populate the subj2obj and obj2subj dicst with (prop, [objs]) or 206 | # (prop, [subjs]) tuples 207 | # flag var to check if the property already added to a node 208 | propFound = False 209 | 210 | # TODO: make FIRST and SECOND blocks more elegant 211 | # FIRST: populate subj2obj 212 | if o_subj not in self.subj2obj: 213 | self.subj2obj[o_subj] = [(o_prop, [o_obj])] 214 | # if subj entity already seen in the graph 215 | else: 216 | # we need to do something smart now 217 | # loop through all already added (prob, [obj]) tuples 218 | for i, (p, o) in enumerate(self.subj2obj[o_subj]): 219 | # if the prop already exists, append to the list of object 220 | if p == o_prop: 221 | propFound = True 222 | # get the list and append to it 223 | self.subj2obj[o_subj][i][1].append(o_obj) 224 | break 225 | # if the search failed, add a new (prop, [obj]) tuple 226 | if not propFound: 227 | self.subj2obj[o_subj].append((o_prop, [o_obj])) 228 | 229 | # SECOND: populate obj2subj 230 | # flag var to check if the property already added to a node 231 | propFound = False 232 | 233 | if o_obj not in self.obj2subj: 234 | self.obj2subj[o_obj] = [(o_prop, [o_subj])] 235 | # if subj entity already seen in the graph 236 | else: 237 | # we need to do something smart now 238 | # loop through all already added (prob, [subj]) tuples 239 | for i, (p, s) in enumerate(self.obj2subj[o_obj]): 240 | # if the prop already exists, append to the list of object 241 | if p == o_prop: 242 | propFound = True 243 | self.obj2subj[o_obj][i][1].append(o_subj) 244 | break 245 | 246 | # if the search failed, add a new (prop, [obj]) tuple 247 | if not propFound: 248 | self.obj2subj[o_obj].append((o_prop, [o_subj])) 249 | 250 | 251 | def delexicalize_text(self, advanced=False): 252 | """ 253 | Apply delexicalization on text. Return delexicaled text. 254 | :para advanced: turn on advanced string similarity procedure 255 | (dtype: bool, default: False) 256 | """ 257 | no_match_list = [] 258 | original_text = ' '.join(re.sub('\s+',' ', self.lexicalization).split()) 259 | delex_text = original_text 260 | 261 | # loop over each entity, find its match in the text 262 | for entity in self.entities.values(): 263 | # flag var, set to True if the procedure finds a match 264 | matchFound = False 265 | 266 | # remove quotes from the entity string 267 | entity_str = entity.mlex_form.replace('"', '') 268 | 269 | # Simple text matching 270 | # 1. try exact matching 1258 271 | if entity_str in self.lexicalization: 272 | delex_text = delex_text.replace(entity_str, 273 | ' ' + entity.ID + ' ') 274 | matchFound = True 275 | 276 | # 2. try lowercased search 1122 277 | elif entity_str.lower() in self.lexicalization.lower(): 278 | start_idx = delex_text.lower().find(entity_str.lower()) 279 | end_idx = start_idx + len(entity_str) 280 | 281 | delex_text = delex_text[:start_idx] + ' ' \ 282 | + entity.ID + ' ' + delex_text[end_idx + 1:] 283 | matchFound = True 284 | 285 | # 3. Try handling entities with the subtring (semanticType) 1006 286 | # e.g. Ballistic (comicsCharacter) --> Ballistic 287 | elif entity_str.endswith(')'): 288 | left_idx = entity_str.find('(') 289 | 290 | entity_str = entity_str[:left_idx].strip() 291 | 292 | if entity_str in self.lexicalization: 293 | delex_text = delex_text.replace(entity_str, 294 | ' ' + entity.ID + ' ') 295 | matchFound = True 296 | 297 | 298 | # if search succeeded, go to next entity, otherwise keep searching 299 | if matchFound or not advanced: 300 | continue 301 | 302 | # simple search not succeeded, do non-trivial text matching 303 | # 4. try date format handling 304 | if text_utils.is_date_format(entity_str): 305 | entity_ngrams = text_utils.find_ngrams(self.lexicalization) 306 | 307 | entity_ngrams = [text_utils.tokenize_and_concat(' '.join(ngram)) 308 | for ngram in entity_ngrams] 309 | 310 | date_strings = [d_str for d_str in entity_ngrams 311 | if text_utils.is_date_format(d_str)] 312 | 313 | # sort data strings by length, get the longest match 314 | date_strings.sort(key=len, reverse=True) 315 | 316 | if date_strings: 317 | best_match = date_strings[0] 318 | delex_text = text_utils.tokenize_and_concat(delex_text) 319 | delex_text = delex_text.replace(best_match, ' ' + entity.ID + ' ') 320 | 321 | matchFound = True 322 | 323 | # 5. try abbreviation handling 324 | # if entity_str contains more than one capitalized word, try to find 325 | # a potential abbreviation of it in the text 326 | if len(text_utils.get_capitalized(entity_str)) > 1 and not matchFound: 327 | # from the entity string, make a list of possible abbreviations 328 | abbr_candidates = text_utils.generate_abbrs(entity_str) 329 | abbr_candidates.sort(key=len, reverse=True) 330 | 331 | # get a list of unigrams in the text sentence 332 | text_unigrams = text_utils.find_ngrams(self.lexicalization, N=1) 333 | text_unigrams = [' '.join(unigram) for unigram in text_unigrams] 334 | 335 | for abbr in abbr_candidates: 336 | # make sure candidate abbr contains more than 1 capital letter 337 | nCaps = len([c for c in abbr if c.isupper()]) 338 | 339 | if abbr in text_unigrams and nCaps > 1: # SUCCESS 340 | print('before:', entity_str, abbr, delex_text) 341 | delex_text = delex_text.replace(abbr, ' ' + entity.ID + ' ') 342 | print('after:', entity_str, abbr, delex_text) 343 | matchFound = True 344 | 345 | # 6. try character-level string matching (last hope) 346 | if not matchFound: 347 | delex_text = text_utils.tokenize_and_concat(delex_text) 348 | best_match = text_utils.find_best_match(entity_str, delex_text) 349 | 350 | if best_match: 351 | delex_text = delex_text.replace(best_match, 352 | ' ' + entity.ID + ' ') 353 | 354 | matchFound = True 355 | 356 | if not matchFound: 357 | no_match_list.append((entity_str, self.lexicalization)) 358 | 359 | final_delex = text_utils.tokenize_and_concat(delex_text) 360 | 361 | # make sure the text ends with a period 362 | final_delex = final_delex if final_delex[-1] == '.' else final_delex + ' .' 363 | 364 | return final_delex # , no_match_list 365 | 366 | 367 | def get_entityGraph(self): 368 | """ 369 | Return a dict of entities and thier outgoing edges. 370 | """ 371 | return self.subj2obj 372 | 373 | 374 | def linearize_graph(self, structured=False, incoming_edges=False): 375 | """ 376 | Generate a linear sequence representing the graph from the triple set 377 | (flat sequence) or from the entity graphs (structured sequence). 378 | """ 379 | 380 | if not structured: 381 | seq = '' 382 | 383 | # to generate a flat sequence, linearize rdf_triples 384 | for triple in self.o_rdf_triples: 385 | # extract nodes (entities) and edges (properties) 386 | subj = triple.subject 387 | obj = triple.object 388 | prop = triple.property 389 | 390 | seq = ' '.join( 391 | [ 392 | seq, 393 | self.entities[subj].ID, 394 | self.entities[subj].stype, 395 | self.properties[prop].mlex_form, 396 | self.entities[obj].ID, 397 | self.entities[obj].stype, 398 | ] 399 | ) 400 | else: 401 | # to generate a structured sequence, linearize entity graphs 402 | if incoming_edges: 403 | entityGraph = self.obj2subj 404 | else: 405 | entityGraph = self.subj2obj 406 | 407 | seq = '(' 408 | 409 | for (attr, val) in entityGraph.items(): 410 | seq = ' '.join([seq, '(']) 411 | seq = ' '.join( 412 | [ 413 | seq, 414 | self.entities[attr].ID, 415 | self.entities[attr].stype 416 | ] 417 | ) 418 | 419 | for prop, obj_list in val: 420 | seq = ' '.join([seq, '(', self.properties[prop].mlex_form]) 421 | 422 | for obj in obj_list: 423 | seq = ' '.join( 424 | [ 425 | seq, 426 | '(', 427 | self.entities[obj].ID, 428 | self.entities[obj].stype, 429 | ')' 430 | ] 431 | ) 432 | seq = ' '.join([seq, ')']) 433 | seq = ' '.join([seq, ')']) 434 | seq = ' '.join([seq, ')']) 435 | 436 | return seq.lstrip() 437 | 438 | 439 | def test(): 440 | xml_str = """ 441 | Donald Trump | birthPlace | USA 442 | USA | leaderName | Donald Trump 443 | USA | capital | Washington DC 444 | Donald Trump | spouse | Melania Knauss 445 | Melania Knauss | nationality | Slovenia 446 | Melania Knauss | nationality | USA 447 | Melania Knauss | birthDate | "1923-11-18" 448 | """ 449 | 450 | otriple_set = et.fromstring(xml_str) 451 | mtriple_set = et.fromstring(xml_str) 452 | 453 | s = """Donald Trump was born in the United States of Amercia, 454 | the country where he later became the president . The captial of the US 455 | is Washington DC . Donald Trump 's wife , Melania Knauss , has two 456 | nationalities ; American and Slovenian . Melania was 457 | born on 18th of November 1923 .""".replace('\n', '') 458 | 459 | t_org = rdf_utils.Tripleset() 460 | t_org.fill_tripleset(otriple_set) 461 | 462 | t_mod = rdf_utils.Tripleset() 463 | t_mod.fill_tripleset(otriple_set) 464 | 465 | test_case = FactGraph(([t_org], t_mod), s) 466 | 467 | print('Properties: ', [*test_case.properties]) 468 | print('Entities: ', [*test_case.entities]) 469 | print('subj2obj Graph: ', test_case.subj2obj) 470 | print('obj2subj Graph: ', test_case.obj2subj) 471 | print('Linearization: ', test_case.linearize_graph()) 472 | print('Strucutred [1]:', 473 | test_case.linearize_graph(structured=True)) 474 | print('Strucutred [2]:', 475 | test_case.linearize_graph(structured=True, incoming_edges=True)) 476 | 477 | delex = test_case.delexicalize_text(advanced=True) 478 | print('Lexicalisation:', delex) 479 | 480 | for p in test_case.properties.values(): 481 | print(p.mlex_form, p.type_form, p.domain, p.range) 482 | 483 | assert test_case.entities.keys() == \ 484 | {'Donald Trump', 'USA', 'Washington DC', 'Melania Knauss', \ 485 | 'Slovenia', '"1923-11-18"'}, \ 486 | "Test case failed! Entities do not match." 487 | 488 | assert test_case.properties.keys() == \ 489 | {'leaderName', 'birthPlace', 'capital', 'spouse', 'nationality', 'birthDate'}, \ 490 | "Test case failed! Properties do not match." 491 | 492 | assert test_case.subj2obj == \ 493 | {'Donald Trump': [ 494 | ('birthPlace', ['USA']), 495 | ('spouse', ['Melania Knauss']) 496 | ], 497 | 'USA': [ 498 | ('leaderName', ['Donald Trump']), 499 | ('capital', ['Washington DC']) 500 | ], 501 | 'Melania Knauss': [ 502 | ('nationality', ['Slovenia', 'USA']), 503 | ('birthDate', ['"1923-11-18"']) 504 | ] 505 | }, "Test case failed! entityGraph does not match." 506 | 507 | print('\nTesting SUCCESSFUL!') 508 | 509 | def main(): 510 | test() 511 | 512 | if __name__ == '__main__': 513 | main() 514 | 515 | 516 | python generate_train_dataset.py -path ../challenge_data_train_dev/train -src_mode flat -src ../datasetsX/train.src -tgt ../datasetsX/train.tgt 517 | -------------------------------------------------------------------------------- /metadata/entities.list: -------------------------------------------------------------------------------- 1 | "737"^^xsd:nonNegativeInteger 2 | 27367194 3 | Slavey_language 4 | American_Craftsman 5 | Arabic 6 | "1998-03-16"^^xsd:date 7 | "1928 PC"@en 8 | Magik_(rapper) 9 | Airports_Authority_of_India 10 | Iraqi_dinar 11 | "5.60937E8"^^ 12 | Daniele_Zoratto 13 | "PR6031.O867"@en 14 | Saint_Anne,_Alderney 15 | Australia 16 | "Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins"@en 17 | Christian_alternative_rock 18 | "compressed rice cooked inbanana leafwith vegetables or minced meat fillings" 19 | "Acta Math. Hungar."@en 20 | Postmodern_architecture 21 | Taquirari 22 | "Concert and events venue"@en 23 | Tarō_Asō 24 | 57392246 25 | Alvis_Speed_25 26 | 23rd_Street_(Manhattan) 27 | Tejano 28 | Fall_Creek_Township,_Madison_County,_Indiana 29 | 1634:_The_Galileo_Affair 30 | "State Senate"@en 31 | United_Kingdom 32 | Illinois 33 | Jusuf_Kalla 34 | Hatchback 35 | "Dave Laughlin"@en 36 | "3500"^^xsd:nonNegativeInteger 37 | "8.3"^^xsd:double 38 | Orange_County,_California 39 | Central_Español 40 | 1962 41 | Folk_rock 42 | Alcatraz_Versus_the_Scrivener's_Bones 43 | Tranmere_Rovers_F.C. 44 | International_Tennis_Federation 45 | "23900"^^xsd:nonNegativeInteger 46 | "1872.69"^^xsd:double 47 | "Mark Desmond"@en 48 | FC_Tom_Tomsk 49 | Alvis_Speed_25__6 50 | "Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road"@en 51 | Nigerian_Air_Force 52 | "Admin. Sci. Q." 53 | Paul_Gustavson 54 | "2000.0"^^ 55 | Maya_Rudolph 56 | Levan_Khomeriki 57 | Roy_Thomas 58 | Kornelije_Kovač 59 | Visayas 60 | "121.92"^^xsd:double 61 | Warsaw 62 | "1927-07-23"^^xsd:date 63 | Russian_Football_National_League 64 | Jerry_Ordway 65 | "286.4526850031616"^^ 66 | Pakistan 67 | Association_of_American_Universities 68 | 3000 69 | "83.82"^^xsd:double 70 | DeSoto_Custom 71 | "Amsterdamsche Football Club Ajax"@en 72 | 334 73 | Cornish_language 74 | S.S.D._Potenza_Calcio 75 | Aarhus_Airport 76 | "0236-5294" 77 | SV_Babelsberg_03 78 | "21.2"^^xsd:double 79 | Apollo_8 80 | Mamnoon_Hussain 81 | Church_of_Denmark 82 | Parliament_of_Catalonia 83 | "1982"^^xsd:gYear 84 | 2014–15_Bundesliga 85 | Frank_de_Boer 86 | "523.951"^^xsd:double 87 | AIDA_Cruises 88 | Harrietstown,_New_York 89 | "0-670-03380-4" 90 | Turkmenistan_Airlines 91 | Old_Man_Gloom 92 | "Cleveland, Ohio 44114"@en 93 | 1635:_The_Cannon_Law 94 | First_Vienna_FC 95 | Helmut_Jahn 96 | Monarchy_of_Denmark 97 | "Alkmaar Zaanstreek"@en 98 | Italians 99 | Alfred_Moore_Scales 100 | Cottage_cheese 101 | Battle_of_Gettysburg 102 | Sammy_Price 103 | Greeks 104 | Adams_Township,_Madison_County,_Indiana 105 | Runcorn_F.C._Halton 106 | Hardcover 107 | "1473-5571" 108 | Al-Khor_Sports_Club 109 | "Pennsylvania"@en 110 | Aleksandr_Prudnikov 111 | New_Hampshire 112 | "1911"^^xsd:gYear 113 | Abdul_Halim_of_Kedah 114 | Alderney 115 | Aberdeen 116 | "Michael Manley"@en 117 | "306.019"^^xsd:double 118 | Manitoba 119 | Nashville,_Tennessee 120 | ACF_Fiorentina 121 | "167.945"^^xsd:double 122 | "5700.0"^^xsd:double 123 | Ampara_District 124 | "Rear Admiral in the Argentine Navy"@en 125 | "7.9"^^ 126 | Mid-Atlantic_Regional_Spaceport_Launch_Pad_0 127 | "1990"^^xsd:gYear 128 | "Türk Şehitleri Anıtı"@en 129 | "07/25" 130 | Vietnamese_language 131 | 3500 132 | Mulatu_Teshome 133 | Valery_Petrakov 134 | "27 June 2015 (JD2457200.5)" 135 | Ampara 136 | FC_Dinamo_Batumi 137 | "100305.0"^^ 138 | "Jepson Way,"@en 139 | "3684.0"^^xsd:double 140 | Doug_Moench 141 | Into_Battle_(novel) 142 | "179.0"^^ 143 | "60"^^xsd:positiveInteger 144 | FC_Zestafoni 145 | James_Pain 146 | Sumitra_Mahajan 147 | "STV"@en 148 | Maccabi_Tel_Aviv_B.C. 149 | George_Winkler 150 | Richard_A._Teague 151 | Irish_people 152 | Arlington,_Texas 153 | "39500.0"^^xsd:double 154 | Igorot_people 155 | "13017.0"(minutes) 156 | Drum_and_bass 157 | Prime_Minister_of_Azerbaijan 158 | Charles_Elkin_Mathews 159 | "192.0"^^xsd:double 160 | "32024459" 161 | Seattle 162 | Dan_Horrigan 163 | "13/31" 164 | "39" 165 | ALV_X-1 166 | Isaac_Rojas 167 | Derbyshire 168 | Raisin 169 | Sri_Lankan_rupee 170 | Max_Huiberts 171 | Fantasy_literature 172 | "1928 SJ"@en 173 | Georgian_architecture 174 | South_Capitol_Street 175 | Preußisch_Oldendorf 176 | "1773.0"^^xsd:double 177 | Danish_language 178 | A_Glastonbury_Romance 179 | Honda 180 | "04/22 'Oostbaan'" 181 | "1121.05"^^xsd:double 182 | 110_Lydia 183 | A.C._Lumezzane 184 | "1-56947-301-3" 185 | FC_Barcelona 186 | 1461102 187 | Cesena 188 | Abhandlungen_aus_dem_Mathematischen_Seminar_der_Universität_Hamburg 189 | Luanda 190 | French_people 191 | 737 192 | Beef_kway_teow 193 | Courcouronnes 194 | "1360-0443" 195 | Rochdale 196 | European_Parliament 197 | Black_Knight_(Dane_Whitman) 198 | Artur_Rasizade 199 | "Nationwide, also can be found in Malaysia and Singapore"@en 200 | "KABI" 201 | Eastern_Province,_Sri_Lanka 202 | Socialist_Republic_of_Serbia 203 | Shuvalan 204 | Palace_of_Westminster 205 | Ranil_Wickremesinghe 206 | Philippe_of_Belgium 207 | Defender_(association_football) 208 | 103_Colmore_Row 209 | "B.M. Reddy"@en 210 | Granola 211 | Iraq 212 | "Bimonthly"@en 213 | A-Rosa_Luna 214 | "125.28"^^xsd:double 215 | Folk_music_of_Ireland 216 | Giuseppe_Iachini 217 | 2014–15_North_West_Counties_Football_League 218 | "Breaz Valer Daniel"@en 219 | "Sold to the Netherlands 1 April 1948" 220 | Olympia,_Washington 221 | "Bread, almonds, garlic, water, olive oil"@en 222 | Hull_City_A.F.C. 223 | Death_metal 224 | Ajax_Youth_Academy 225 | Christmas_pudding 226 | Washington_(state) 227 | "682, 817,214, 469, 972" 228 | Prokopis_Pavlopoulos 229 | "1966-02-28" 230 | "545.897"^^xsd:double 231 | "Fish cooked in sour and hot sauce" 232 | Netherlands_national_football_team 233 | 3.9447e+07 234 | 159 235 | Anderson,_Indiana 236 | Abner_(footballer) 237 | Ministry_of_Economy,_Development_and_Tourism_(Greece) 238 | R.S.C._Anderlecht 239 | Germans 240 | 770404678 241 | Pickard_Chilton 242 | "1969-09-01"^^xsd:date 243 | Seminary_Ridge 244 | Jens_Härtel 245 | Navajo_language 246 | "400.0"^^ 247 | "6.603633E9"^^ 248 | SV_Werder_Bremen 249 | Sandesh_(confectionery) 250 | "1931" 251 | "Legion of Merit ribbon.svg" 252 | Riverside_Art_Museum 253 | "with metal joints (arranged in a straight line), spiral bevel fully floating back axle" 254 | "Chișinău, Moldova"@en 255 | "18.52"^^xsd:double 256 | Lufkin,_Texas 257 | "Yuzhnoye Design Bureau"@en 258 | DeSoto_Firedome 259 | France 260 | "1989-05-09"^^xsd:date 261 | Ahmedabad 262 | "17R/35L" 263 | Central_Denmark_Region 264 | "3684.12"^^xsd:double 265 | "2744.0"^^xsd:double 266 | Adam_McQuaid 267 | Stephen_Dilts 268 | Adirondack_Regional_Airport 269 | Olympiacos_F.C. 270 | Alpena_County_Regional_Airport 271 | "−7" 272 | Rafael_Viñoly 273 | Candombe 274 | "83646315" 275 | Ahmad_Kadhim_Assad 276 | 78771100 277 | "247.0"^^xsd:double 278 | "KACY" 279 | 1929 280 | Poales 281 | "1933-10-17"^^xsd:date 282 | Emile_Roemer 283 | "929.0"^^xsd:double 284 | Jack_Kirby 285 | "South Runway" 286 | SC_Wiener_Neustadt 287 | "Government of Addis Ababa"@en 288 | "2777.0"^^xsd:double 289 | Mexican_peso 290 | "28.0"^^ 291 | 94 292 | "American Defense Service ribbon.svg"@en 293 | Dáil_Éireann 294 | "06/24 'Kaagbaan'" 295 | A_Long_Long_Way 296 | "5.0"^^ 297 | "247.0"^^ 298 | "318875313" 299 | "1998-07-21"^^xsd:date 300 | "1.524"^^xsd:double 301 | Roger_McKenzie_(comics) 302 | Alba_County 303 | Abdul_Rahman_Ya'kub 304 | Aaron_Turner 305 | Juan_Perón 306 | Iraq_national_under-23_football_team 307 | The_Secret_Scripture 308 | Wheeler,_Texas 309 | "Rome, Italy"@en 310 | Hays_County,_Texas 311 | "1509.98"^^xsd:double 312 | Augustus_Pugin 313 | Reidsville,_North_Carolina 314 | Pork_belly 315 | Henry_Clay 316 | Paul_Ryan 317 | Airbus_Group 318 | Pound_sterling 319 | 2014–15_Lega_Pro 320 | Valentina_Matviyenko 321 | Curitiba 322 | "American Motors Matador"@en 323 | 2014–15_A_EPSTH,_Greece 324 | Italy_national_football_team 325 | Opelika,_Alabama 326 | Mikulov 327 | Pop_music 328 | Accrington 329 | Addis_Ababa 330 | "8.0"^^xsd:double 331 | "April 2014" 332 | "A.E Dimitra Efxeinoupolis"@en 333 | "1901-03-04"^^xsd:date 334 | Estadio_Jorge_Calero_Suárez 335 | Asser_Levy_Public_Baths 336 | Mumbai 337 | ENAIRE 338 | "and all-syncromesh, centre change lever, open tubular propellor shaft" 339 | Verona 340 | "5600.0"^^xsd:double 341 | Anaheim,_California 342 | Felipe_González 343 | "30.0"^^ 344 | "1/19" 345 | County_of_Tyrol 346 | Humphrys,_Tennant_and_Dykes 347 | Telangana 348 | Albuquerque_City_Council 349 | Giovanni_Bertone 350 | "UT Austin, B.S. 1955" 351 | 2.79142e+11 352 | New_York_City 353 | United_States_Ambassador_to_Norway 354 | Alaa_Abdul-Zahra 355 | 107_Camilla 356 | El_Salvador_national_football_team 357 | "967.435"^^xsd:double 358 | 4.14e+06 359 | "16.55"^^ 360 | National_Hockey_League 361 | "ASCQAG"@en 362 | Stadio_Pietro_Barbetti 363 | Community_of_Madrid 364 | Alfons_Gorbach 365 | Guitar 366 | "88.0"^^xsd:double 367 | ELA-3 368 | Dave_Laughlin 369 | "Supermini"@en 370 | Basil 371 | "1966"^^xsd:gYear 372 | "2014-10-28"^^xsd:date 373 | A_Loyal_Character_Dancer 374 | Arem-arem 375 | "Alan LaVern Bean"@en 376 | "noodles,porkorgans,vegetables,chicken,shrimp,beef" 377 | "Associazione Calcio Lumezzane SpA"@en 378 | A.F.C._Fylde 379 | Makis_Voridis 380 | Supermini 381 | Niger_State 382 | "15/33" 383 | "1999-05-29"^^xsd:date 384 | Swiss_Psalm 385 | Thomas_Doll 386 | Yugoslavia 387 | Malay_Peninsula 388 | "\"Squeezed\" or \"smashed\" fried chicken served with sambal"@en 389 | The_Wildweeds 390 | Simon_&_Schuster 391 | (19255)_1994_VK8 392 | Bloomsbury 393 | Dallas 394 | "1.2E8"^^ 395 | "30843.8"^^xsd:double 396 | Provincial_Assembly_of_the_Punjab 397 | "05L/23R" 398 | Kingdom_of_Sarawak 399 | Saskatchewan 400 | Gujarat_Legislative_Assembly 401 | "Bo Bibbowski"@en 402 | Parliament_of_the_United_Kingdom 403 | Nigerian_Army 404 | 4.59723e+07 405 | C.D._Águila 406 | "Amsterdamsche Football Club Ajax Amateurs"@en 407 | "1856"^^xsd:gYear 408 | "Nationwide in Singapore and Indonesia"@en 409 | Mike_Akhigbe 410 | "James Pain and George Richard Pain,"@en 411 | Aaron_Boogaard 412 | RoPS 413 | Royal_Artillery 414 | "Drake Stevens"@en 415 | Colwyn_Bay_F.C. 416 | Alvah_Sabin 417 | "214.0"^^xsd:double 418 | Laurales 419 | Kurdish_languages 420 | Soup 421 | Glen_Ridge,_New_Jersey 422 | Göttingen 423 | Zvi_Sherf 424 | "609" 425 | Dance-pop 426 | C.D._FAS 427 | Travis_County,_Texas 428 | Penguin_Random_House 429 | "5.3"^^xsd:double 430 | "Vincent H. Crespi, Bernard S. Gerstman, A.T. Charlie Johnson, Masaaki Tanaka, Enge G. Wang"@en 431 | New_Mexico_House_of_Representatives 432 | Soviet_Union_national_football_team 433 | Ayam_penyet 434 | AFC_Ajax_N.V. 435 | "09L/27R" 436 | Elliot_See 437 | South_Region,_Brazil 438 | "18.0"^^ 439 | "1411.0"^^xsd:double 440 | 3.11291e+07 441 | Ministry_of_Health,_Welfare_and_Sport_(Netherlands) 442 | The_Grantville_Gazette 443 | Real_Madrid_C.F. 444 | Italy_national_under-16_football_team 445 | Aleksandre_Guruli 446 | 69618 447 | Asian_South_Africans 448 | PSV_Eindhoven 449 | Alianza_F.C. 450 | Blackpool_F.C. 451 | 8002 452 | Wolf_Solent 453 | Jakarta 454 | Al_Anderson_(NRBQ) 455 | Adam_Holloway 456 | "252000.0"^^ 457 | Battle_of_the_Wilderness 458 | Mayor_of_Stamford,_Connecticut 459 | Malaysian_Indian 460 | Houston_Texans 461 | Genoa 462 | Akeem_Dent 463 | "Fort Campbell, KY, raised in Dothan, AL"@en 464 | Thames,_New_Zealand 465 | Acura_TLX__10 466 | Dale,_Wisconsin 467 | Adare 468 | Rear_admiral 469 | Ground_beef 470 | Bury_F.C. 471 | 1634:_The_Baltic_War 472 | Alcobendas 473 | Margrethe_II_of_Denmark 474 | "2013-04-21"^^xsd:date 475 | "Admin. Sci. Q."@en 476 | "AMAHE9" 477 | 2011_PDL_season 478 | "657/714" 479 | "52.0"^^ 480 | Bacon_sandwich 481 | Juan_Afara 482 | Black_metal 483 | "1868-09-07"^^xsd:date 484 | Cape_Canaveral_Air_Force_Station 485 | Benitoite 486 | "2001-03-01"^^xsd:date 487 | Chocolate 488 | "2" 489 | 507 490 | DuPage_County,_Illinois 491 | Synthpop 492 | "72.0"^^xsd:double 493 | Baked_Alaska 494 | Jacob_Zuma 495 | "94.0"^^xsd:double 496 | Audi_A1__5 497 | "1974-08-01" 498 | Wizards_at_War 499 | 1904 500 | Bundelkhand 501 | Tacoma,_Washington 502 | "3.8"^^xsd:double 503 | Abdulsalami_Abubakar 504 | Indian_people 505 | "0.0473"^^ 506 | Sponge_cake 507 | "25.0"^^xsd:double 508 | Daniel_Webster 509 | "Retired"@en 510 | Livorno 511 | "ABI"@en 512 | SV_Werder_Bremen_II 513 | Khalid_Mahmood_(British_politician) 514 | "900.074"^^xsd:double 515 | Dead_Man's_Plack 516 | Bandeja_paisa 517 | Abner_W._Sibal 518 | "345.948"^^xsd:double 519 | Indian_rupee 520 | FSV_Zwickau 521 | Vandenberg_Air_Force_Base 522 | Brandon,_Manitoba 523 | "911.0"^^xsd:double 524 | "2004-07-14"^^xsd:date 525 | "Edwin Eugene Aldrin Jr." 526 | "4.8"^^xsd:double 527 | Abraham_A._Ribicoff 528 | Otkrytiye_Arena 529 | Mahé,_India 530 | Labour_Party_(UK) 531 | "100"^^xsd:nonNegativeInteger 532 | Broadcasting_House 533 | Plastik_Mak 534 | Carrarese_Calcio 535 | "3300.0"^^xsd:double 536 | Adonis_Georgiadis 537 | China 538 | New_Mexico_Territory 539 | Solanales 540 | Indonesian_rupiah 541 | SK_Rapid_Wien 542 | Computer_science 543 | "10R/28L" 544 | Audi_A1__3 545 | Federal_Chancellor_of_Switzerland 546 | "Associazione Calcio ChievoVerona S.r.l."@en 547 | Porius:_A_Romance_of_the_Dark_Ages 548 | Abilene_Regional_Airport 549 | Duncan_Rouleau 550 | "184.099"^^xsd:double 551 | Turkmenistan 552 | BBC 553 | "SLK"@en 554 | "2158-3226" 555 | Jinnah_International_Airport 556 | "0.02"^^ 557 | "noodles, pork organs, vegetables, chicken, shrimp, beef"@en 558 | "Acta Palaeontol. Pol."@en 559 | "45644811" 560 | "5000"^^xsd:nonNegativeInteger 561 | Deram_Records 562 | Ferencvárosi_TC 563 | "~700"@en 564 | Sidcup 565 | "2743.0"^^xsd:double 566 | "75.324"^^ 567 | Soul_music 568 | "2701.75"^^xsd:double 569 | Fried_egg 570 | "3746.66"^^xsd:double 571 | Aston_Martin_DBS 572 | DeSoto_(automobile) 573 | "SAGE Publications for the Samuel Curtis Johnson Graduate School of Management, Cornell University"@en 574 | American_Locomotive_Company 575 | 1.40159e+08 576 | "Benjamin Urich"@en 577 | Brazilians_in_Japan 578 | Anderson_Township,_Madison_County,_Indiana 579 | AC_Hotel_Bella_Sky_Copenhagen 580 | Trenton,_New_Jersey 581 | "5.2395158233968E8"^^ 582 | John_Roberts 583 | 1101_Clematis 584 | Gadzhi_Gadzhiyev 585 | Soho_Press 586 | "61.8744"^^xsd:double 587 | Austin_Fernando 588 | "Member of theCongress of Deputies" 589 | "64.008"^^xsd:double 590 | "and"@en 591 | "1200000.0"^^ 592 | Garlic 593 | Technical_Institute,_Kaduna 594 | Bozo_the_Iron_Man 595 | Administrative_Science_Quarterly 596 | Georgia,_Vermont 597 | Ampara_Hospital 598 | Christian_Burns 599 | The_Horns_of_Happiness 600 | "24.9936"^^xsd:double 601 | "ATISET"@en 602 | "3.04 m"@en 603 | "Acta Math. Hungar." 604 | "746.0"^^xsd:double 605 | "1978" 606 | "Onion, garlic, black pepper, chili"@en 607 | Spaceport_Florida_Launch_Complex_36 608 | "2013-03-11"^^xsd:date 609 | "265.0"^^ 610 | List_of_counties_in_Texas 611 | Batak 612 | "Bread and bacon, with a condiment, often ketchup or brown sauce"@en 613 | Parkersburg,_West_Virginia 614 | Aaron_Hunt 615 | Logan_Township,_Fountain_County,_Indiana 616 | "512 & 737" 617 | Almond 618 | Malaysia 619 | Poland 620 | "ADICE5"@en 621 | Mexican_Spanish 622 | Udinese_Calcio 623 | House_of_Representatives_(Netherlands) 624 | Alan_Frew 625 | "3/21" 626 | Tyrol_(state) 627 | "1872"^^xsd:gYear 628 | "North Wall Quay"@en 629 | "292"^^xsd:positiveInteger 630 | "1998-07-21" 631 | Ashgabat_International_Airport 632 | Spain 633 | "179.0"^^xsd:double 634 | 3 635 | Atatürk_Monument_(İzmir) 636 | "4.41092E8"^^ 637 | Collin_County,_Texas 638 | "Deceased" 639 | Twilight_(band) 640 | Hailemariam_Desalegn 641 | "45"^^xsd:positiveInteger 642 | Paul_Kupperberg 643 | "Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, fried shallots"@en 644 | 4.41092e+11 645 | Lettuce 646 | Dodge 647 | Finns 648 | 2.046e+08 649 | Turkish_lira 650 | "AZAL Peşəkar Futbol Klubu"@en 651 | "4150"^^xsd:nonNegativeInteger 652 | Paperback 653 | "2.5498957060815E8"^^ 654 | Brussels 655 | Marriott_International 656 | Dothan,_Alabama 657 | "120770.0"^^ 658 | "1981.5"^^xsd:double 659 | "18L/36R" 660 | Motherwell_F.C. 661 | Guarania_(music) 662 | 2014–15_Azerbaijan_Premier_League 663 | England 664 | "173.52"^^xsd:double 665 | 1634:_The_Ram_Rebellion 666 | Salem,_Oregon 667 | "30.0"^^xsd:double 668 | Magdalene_College,_Cambridge 669 | "2989.0"^^xsd:double 670 | "Mayor of Stamford, Connecticut" 671 | Torino_F.C. 672 | Yuzhnoye_Design_Office 673 | Colombian_cuisine 674 | "Lambien"@en 675 | Ahmet_Ertegun 676 | Washington,_D.C. 677 | "23.0"^^xsd:double 678 | "1981-02-04"^^xsd:date 679 | "issues II, III, IV, V and VI"@en 680 | "4.3717E8"^^ 681 | Paraguay 682 | Unione_Triestina_2012_S.S.D. 683 | Bhangra_(music) 684 | Joseph_Stalin 685 | K-W_United_FC 686 | Cooking_plantain 687 | "2702.0"^^xsd:double 688 | Academy_of_Comic_Book_Arts 689 | Andrew_the_Apostle 690 | Vermont's_3rd_congressional_district 691 | "3.7124E8"^^ 692 | Native_Americans_in_the_United_States 693 | "6.120048890337E8"^^ 694 | "Brussels, Belgium"@en 695 | "185.42"^^ 696 | Columbus,_Ohio 697 | Haarlemmermeer 698 | "2009-06-01"^^xsd:date 699 | HIV 700 | "2.0"^^ 701 | FC_Terek_Grozny 702 | A.S._Livorno_Calcio 703 | "734" 704 | Peter_Laird 705 | "Roland Desmond"@en 706 | "7.62"^^xsd:double 707 | Banana_leaf 708 | J._P._McManus 709 | Fort_Worth,_Texas 710 | "Annie Dunne"@en 711 | "Jorge Humberto Rodríguez"@en 712 | "from Connecticut's 4th district"@en 713 | Arròs_negre 714 | "Volkswagen Polo Mk5"@en 715 | Franklin_County,_Pennsylvania 716 | Georgia_(U.S._state) 717 | "Thomas Pallesen"@en 718 | Parliamentary_group_leader 719 | 404 720 | FK_Austria_Wien 721 | Alan_Tudyk 722 | "Associazione Sportiva Gubbio 1910 Srl"@en 723 | Serie_D 724 | SV_Germania_Schöneiche 725 | Hohne 726 | Greater_Manchester 727 | New_Democracy_(Greece) 728 | Purple_finch 729 | Sheikh_Russel_KC 730 | "2003.0"^^xsd:double 731 | "1972"^^xsd:gYear 732 | Paris_Cullins 733 | Clyde_F.C. 734 | 6.86088e+08 735 | Federal_Assembly_(Switzerland) 736 | "Bronze"@en 737 | "2009" 738 | 1097_Vicia 739 | "9800.0"^^xsd:double 740 | Madison,_Wisconsin 741 | 5000 742 | "Ground beef,tapioca,noodle, ricevermicelli, beefbroth, kailan,celery, salted vegetables, fried shallots" 743 | Steel_Azin_F.C. 744 | Chicharrón 745 | Stanislaw_Tillich 746 | Faber_and_Faber 747 | 102372 748 | "5.57"^^ 749 | "1913"^^xsd:gYear 750 | Alvis_Speed_25__2 751 | Big_Hero_6_(film) 752 | "347.1"^^ 753 | "210.312"^^xsd:double 754 | "1818.0"^^xsd:double 755 | "233"^^xsd:positiveInteger 756 | Coconut_milk 757 | Tom_Lyle 758 | Armin_van_Buuren 759 | Aaron_Bertram 760 | "Hüseyin Bütüner and Hilmi Güner"@en 761 | Kingdom_of_France 762 | Olive_oil 763 | Chiang_Kai-shek 764 | Parc_Olympique_Lyonnais 765 | Linn_County,_Oregon 766 | Frangipane 767 | Turkmenbashi_International_Airport 768 | "814"^^xsd:nonNegativeInteger 769 | "497.0"^^xsd:double 770 | "The Mechanics,"@en 771 | Cleveland 772 | 7.03959e+08 773 | Greek_language 774 | Javanese_cuisine 775 | Christopher_Taylor_(politician) 776 | Abdul_Taib_Mahmud 777 | "1868-08-15"^^xsd:date 778 | Campeonato_Brasileiro_Série_C 779 | Bangalore 780 | Italian_meal_structure 781 | 60 782 | "0-7156-3648-0" 783 | Inuktitut 784 | Lockheed_C-130_Hercules 785 | Punjab,_Pakistan 786 | Trinidad_and_Tobago_national_under-20_football_team 787 | B._V._Doshi 788 | Coupé 789 | Arctech_Helsinki_Shipyard 790 | William_Anders 791 | "45000.0"^^ 792 | Aleksander_Barkov,_Jr. 793 | "2439.01"^^xsd:double 794 | "2006"^^xsd:gYear 795 | Vajubhai_Vala 796 | Italy 797 | 2.96521e+11 798 | Alberto_Teisaire 799 | "Minister for Health" 800 | Vica 801 | John_Wiley_&_Sons 802 | "August 27, 2011 (JD2455800.5)" 803 | "3"^^xsd:positiveInteger 804 | Bucharest 805 | 1981 806 | Nigerian_Navy 807 | Antioquia_Department 808 | Darien,_Connecticut 809 | Limerick_City_and_County_Council 810 | Rock_music 811 | Bolt_(DC_Comics) 812 | 8.82343e+10 813 | "1941-07-03"^^xsd:date 814 | "17068.8"^^ 815 | Pure_mathematics 816 | Vietnamese_people_in_Japan 817 | Austrian_People's_Party 818 | Lockheed_AC-130 819 | "Deputy Parliamentary Spokesman ofPopular Orthodox Rally" 820 | Airman_(comics) 821 | "896.0"^^xsd:double 822 | "United States Air Force"@en 823 | "Indonesia and Malaysia"@en 824 | Albert_B._White 825 | "Lucky Ajax"@en 826 | "1500"^^xsd:nonNegativeInteger 827 | Annie_Dunne 828 | Alison_O'Donnell 829 | "1995"^^xsd:gYear 830 | Singapore 831 | "08/26" 832 | 230 833 | 137 834 | 2014–15_Regionalliga 835 | Brazil 836 | Chinese_cuisine 837 | Julia_Morgan 838 | "1.905"^^xsd:double 839 | Marousi 840 | Abu_Zahar_Ujang 841 | Kent 842 | Katowice 843 | 210 844 | Ponteareas 845 | "Big Hero 6(2014)" 846 | Pleasant_Township,_Steuben_County,_Indiana 847 | Jonathan_Mendelsohn 848 | English_Americans 849 | Hessisch_Oldendorf 850 | "Metapán, El Salvador"@en 851 | Alderney_Airport 852 | Iraq_national_football_team 853 | "Isidro Metapán"@en 854 | "Larry Bolatinsky"@en 855 | Alfredo_Zitarrosa 856 | Essex_County,_New_Jersey 857 | "--03-16"^^xsd:gMonthDay 858 | "8.3 m"@en 859 | "88.392"^^xsd:double 860 | "333.0"^^xsd:double 861 | F._Vilas 862 | Elizabeth_II 863 | Ximo_Puig 864 | "7.08"^^xsd:double 865 | Carroll_County,_Maryland 866 | Ohio 867 | "Denzil Antonio"@en 868 | "St. Benedict's Monastery, Adisham, Haputhale, Sri Lanka"@en 869 | History_of_the_St._Louis_Rams 870 | "1-4-2 Nakadori"@en 871 | 60040714 872 | "2005-05-05"^^xsd:date 873 | Robert_Gates 874 | Marry_Banilow 875 | Malik_Muhammad_Rafique_Rajwana 876 | Retrovirus 877 | "Kway teow , beef tenderloin, gula Melaka ,sliced, dried black beans, garlic, dark soy sauce, lengkuas , oyster sauce, soya sauce, chilli and sesame oil"@en 878 | Stadio_Marc'Antonio_Bentegodi 879 | Lancia_Thema 880 | Billy_Iuso 881 | 1955_Dodge 882 | "248"^^xsd:positiveInteger 883 | Palo_Seco 884 | Marysville,_Ohio 885 | "3400.0"^^xsd:double 886 | Nu_metal 887 | Najib_Razak 888 | Polish_language 889 | Military_Cross 890 | Gene_Colan 891 | "1989-01-01"^^xsd:date 892 | "597.0"^^xsd:double 893 | 5.54e+07 894 | Malaysian_Malay 895 | "-3.3528"^^xsd:double 896 | "1299.0"^^xsd:double 897 | French_language 898 | Bill_Oddie 899 | South_Africa 900 | "144.7"^^ 901 | People's_Party_(Spain) 902 | "Member of theTexas State SenatefromDistrict 4(Port Arthur)" 903 | "62145.25893504"^^ 904 | "52.0"(minutes) 905 | Cornell_University 906 | Michael_Tinkham 907 | Arabian_Sea 908 | "1923-11-18"^^xsd:date 909 | "June 1981"@en 910 | Atlanta_Falcons 911 | "895.807"^^xsd:double 912 | Romania 913 | AWH_Engineering_College 914 | Valencian_Community 915 | 2001 916 | "100000.0"^^xsd:double 917 | Paneer 918 | Agnes_Ward_White 919 | "546.0"^^xsd:double 920 | "Deputy Minister for Development, Competitiveness and Shipping" 921 | "President"@en 922 | "13.1064"^^xsd:double 923 | United_States_invasion_of_Panama 924 | "131.5998858742825"^^ 925 | Milonga_(music) 926 | Jamie_Chung 927 | Scott_Adsit 928 | "3000"^^xsd:nonNegativeInteger 929 | "211.0"^^ 930 | 103_Hera 931 | Aurakles 932 | "1974-03-04"^^xsd:date 933 | Association_for_Computing_Machinery 934 | Rice 935 | "Indonesia, derived from Chinese meat ball"@en 936 | "0567-7920" 937 | General_Dynamics_F-16_Fighting_Falcon 938 | A.S.D._Licata_1931 939 | Apiaceae 940 | "January 2009" 941 | Bacon 942 | A.S._Roma 943 | "250"^^xsd:nonNegativeInteger 944 | Julius_Springer 945 | North_West_Counties_Football_League 946 | "2003.45"^^xsd:double 947 | Batumi 948 | "2214.98"^^xsd:double 949 | Birmingham 950 | Atlas_II 951 | Onion 952 | Madrid 953 | Gérard_Larcher 954 | "−8"@en 955 | "Pakora and other fritters made from wheat or corn flour"@en 956 | President_of_Turkey 957 | "Balder Odinson"@en 958 | "2899.87"^^xsd:double 959 | 32024459 960 | Price_Daniel 961 | St._Louis 962 | Héctor_Numa_Moraes 963 | 2.58222e+08 964 | Matteo_Renzi 965 | "Blue, White and Orange"@en 966 | John_Digweed 967 | "AIP Adv." 968 | Allama_Iqbal_International_Airport 969 | Amarillo,_Texas 970 | "ACF Fiorentina S.p.A."@en 971 | Alexander_L._Wolf 972 | Rover_Company 973 | Aristóteles_Sandoval 974 | Aarhus 975 | "Chief of Defence Staff" 976 | "877.0"^^xsd:double 977 | "2009-10-02"^^xsd:date 978 | "Ground almond, jam, butter, eggs"@en 979 | Eva_Perón 980 | "1987-09-27"^^xsd:date 981 | "1971-07-01"^^xsd:date 982 | Sweden 983 | "1855-05-31"^^xsd:date 984 | 765 985 | "368.65"^^ 986 | Karnataka 987 | Honda_Accord 988 | 25 989 | 1976 990 | Universal_Records_(defunct_record_label) 991 | The_Star-Spangled_Banner 992 | "2.16"^^xsd:double 993 | Al-Zawra'a_SC 994 | "10000"^^xsd:nonNegativeInteger 995 | "01461102" 996 | Boston 997 | Sheldon_Moldoff 998 | Populous_(company) 999 | "4/22" 1000 | Uruguayan_Primera_División 1001 | California_State_Legislature 1002 | Arese 1003 | Sami_languages 1004 | "360"^^xsd:positiveInteger 1005 | 2014–15_Topklasse 1006 | "Governor of Texas"@en 1007 | Hook_'em_(mascot) 1008 | "1202.846"^^ 1009 | "978-0-15-204770-2" 1010 | Disco 1011 | "7.66"^^xsd:double 1012 | "900.0"^^xsd:double 1013 | Luxury_vehicle 1014 | Susana_Díaz 1015 | Euroleague 1016 | Baduy 1017 | "80002709" 1018 | Uruguay 1019 | Cook_County,_Illinois 1020 | "47.8536"^^xsd:double 1021 | Allan_Shivers 1022 | "Asphalt/Concrete" 1023 | "265.15"^^ 1024 | N._R._Pogson 1025 | "Tomatoes, red chili, garlic, olive oil"@en 1026 | V12_engine 1027 | Suburban_Legends 1028 | Ridgewood,_Queens 1029 | Dublin 1030 | Ab_Klink 1031 | "3-speed automatic" 1032 | İstanbulspor_A.Ş. 1033 | Malaysian_Chinese 1034 | Philippine_English 1035 | San_Francisco_Bay_Area 1036 | Aston_Martin_Vantage 1037 | 17023 1038 | Electric_guitar 1039 | "837080.744"^^ 1040 | "5.7"^^ 1041 | Sumac_(band) 1042 | The_Grantville_Gazettes 1043 | Lotus_Eaters_(band) 1044 | Albany,_Oregon 1045 | Bakewell_pudding 1046 | Galicia_(Spain) 1047 | "Member of the Connecticut Senate from the 26th District"@en 1048 | "69618"^^xsd:nonNegativeInteger 1049 | Akita_Prefecture 1050 | Al_Kharaitiyat_SC 1051 | Alan_Bean 1052 | "APGPAC" 1053 | Japan 1054 | "1986-10-12"^^xsd:date 1055 | "185.0"^^ 1056 | Sago 1057 | "Red granite and white marble"@en 1058 | Habbaniyah 1059 | "2439.0"^^xsd:double 1060 | "0.0348"^^ 1061 | "0-439-92550-9" 1062 | "111483.648"^^ 1063 | "PS3523.E55 S4 1982"@en 1064 | United_States_Air_Force 1065 | "50"^^xsd:positiveInteger 1066 | "DeMarce short stories in the"@en 1067 | "0965-2140" 1068 | "UTAA" 1069 | 34 1070 | "11/29" 1071 | "1963"^^xsd:gYear 1072 | Khadija_Arib 1073 | Gangsta_rap 1074 | "1935"^^xsd:gYear 1075 | FC_Kuban_Krasnodar 1076 | "→ Sampdoria"@en 1077 | "Atlanta, Georgia"@en 1078 | American_Journal_of_Mathematics 1079 | FC_Karpaty_Lviv 1080 | "3500.0"^^xsd:double 1081 | "singer, Reality television judge"@en 1082 | Berliner_AK_07 1083 | Malay_cuisine 1084 | "Helmut Jahn"@en 1085 | Kimberly,_Wisconsin 1086 | Andrews_County,_Texas 1087 | Andrew_White_(musician) 1088 | FC_Torpedo_Moscow 1089 | 1878 1090 | La_Paz,_Iloilo_City 1091 | "159.106"^^xsd:double 1092 | "1932-03-15" 1093 | Mark_Sixma 1094 | Tamil_language 1095 | "1999"^^xsd:gYear 1096 | Chinese_language 1097 | "Shuvalan, Baku, Azerbaijan"@en 1098 | Gran_Asunción 1099 | "1732-2421" 1100 | Acta_Palaeontologica_Polonica 1101 | Connecticut_Huskies 1102 | "Print"@en 1103 | Batagor 1104 | Greece 1105 | "solo_singer"@en 1106 | County_Limerick 1107 | Sarawak 1108 | 11264_Claudiomaccone 1109 | "Singapore and Indonesia"@en 1110 | "120.0"^^ 1111 | 2 1112 | "V8"@en 1113 | "4000.0"^^xsd:double 1114 | Akeem_Adams 1115 | (29075)_1950_DA 1116 | Al-Amin_Daggash 1117 | Belgrade 1118 | Floating_World_Records 1119 | Ketchup 1120 | George_MacDonald 1121 | "Istanbul, Turkey"@en 1122 | 2011 1123 | 1._FC_Köln 1124 | "Edwin E. Aldrin, Jr."@en 1125 | Cyril_Ramaphosa 1126 | "Maryland"@en 1127 | Atlantic_City_International_Airport 1128 | 211 1129 | New_Jersey 1130 | "111484.0"^^xsd:double 1131 | Leeds_United_F.C. 1132 | AWD_(vehicle) 1133 | 1955_Dodge__2 1134 | Warner_Music_Group 1135 | "490.9"^^ 1136 | United_States_Congress 1137 | "Port Authority of New York and New Jersey & SJTA"@en 1138 | French_Guiana 1139 | "Blackpool"@en 1140 | Alexandria,_Indiana 1141 | 1999 1142 | 2.88749e+11 1143 | Port_Authority_of_New_York_and_New_Jersey 1144 | "1987"^^xsd:gYear 1145 | "Clerk"@en 1146 | Lentivirus 1147 | Blackburn_Rovers_F.C. 1148 | "3453.0"^^xsd:double 1149 | "1296.654601076267"^^ 1150 | "93645978" 1151 | Veikkausliiga 1152 | Apollo_14 1153 | Republic_of_Ireland 1154 | 1910 1155 | "Ottoman Army soldiers killed in the Battle of Baku"@en 1156 | "Member of theConnecticut Senatefrom the26th District" 1157 | Premier_Development_League 1158 | "1 June 2009" 1159 | Trinidad_and_Tobago_national_football_team 1160 | Vigor_Lamezia 1161 | Al_Asad_Airbase 1162 | "Ahri'ahn"@en 1163 | "2006-12-31"^^xsd:date 1164 | Ahmet_Davutoğlu 1165 | "512"^^xsd:positiveInteger 1166 | (66391)_1999_KW4 1167 | Christian_Heinrich_Friedrich_Peters 1168 | 6.15591e+12 1169 | Mathematics 1170 | Nikos_Voutsis 1171 | ".Asherton, Texas"@en 1172 | 100 1173 | 10000 1174 | "Eric Flint, Virginia DeMarce, et al."@en 1175 | U.S._Città_di_Palermo 1176 | Spanish–American_War 1177 | Ram_Naik 1178 | "I6"@en 1179 | Celery 1180 | "HOK SVE"@en 1181 | "1080-6377" 1182 | Danes 1183 | "1980-04-23"^^xsd:date 1184 | Sergey_Sobyanin 1185 | Auburn,_Alabama 1186 | 250 1187 | FC_Amkar_Perm 1188 | "0.0252"^^ 1189 | Manhattan 1190 | 1983 1191 | "Am. J. Math." 1192 | Adare_Manor 1193 | DeKalb_County,_Georgia 1194 | Taro 1195 | "1099.0"^^xsd:double 1196 | "95040.0"^^xsd:double 1197 | "Joden , Godenzonen"@en 1198 | Shirley,_Derbyshire 1199 | Andrews_County_Airport 1200 | Angola_International_Airport 1201 | Sardar_Ayaz_Sadiq 1202 | Coloured 1203 | Northbrook,_Illinois 1204 | Shiitake 1205 | Aston_Martin_RHAM/1 1206 | "1907-07-11"^^xsd:date 1207 | Opel_Vectra 1208 | "9450994531"^^xsd:decimal 1209 | Aenir 1210 | A.C._Chievo_Verona 1211 | Chili_pepper 1212 | A.C._Cesena 1213 | Arianespace 1214 | Apple 1215 | Aston_Martin_V8__1 1216 | Doris_Bures 1217 | "1.1E8"^^ 1218 | Magistrate 1219 | "1989"^^xsd:gYear 1220 | Burbank,_California 1221 | Lieutenant_colonel 1222 | 27250 1223 | U.S._Route_83 1224 | Jones_County,_Texas 1225 | "AGR" 1226 | Iraq_national_under-20_football_team 1227 | Silvano_Aureoles_Conejo 1228 | 1._FC_Union_Berlin 1229 | Ajoblanco 1230 | "Dane Whitman"@en 1231 | A_Severed_Wasp 1232 | "3.048"^^xsd:double 1233 | Shabab_Al-Ordon_Club 1234 | "Steve Bright"@en 1235 | Battle_of_Mine_Run 1236 | "VAM Classic"@en 1237 | "14R/32L" 1238 | Fighter_pilot 1239 | "Voice, bodhrán, percussion, autoharp"@en 1240 | "18L/36R 'Aalsmeerbaan'" 1241 | Gale_Brewer 1242 | Civil_Aviation_Authority_of_New_Zealand 1243 | "Parti Pesaka Bumiputera Bersatu"@en 1244 | Lamborghini 1245 | BBC_Radio 1246 | Standard_Chinese 1247 | Spata 1248 | "Stellendam, Netherlands"@en 1249 | Hip_hop_music 1250 | "978-1-4165-4253-7" 1251 | Cubanelle 1252 | "Associazione Sportiva Roma S.p.A."@en 1253 | Aleksandra_Kovač 1254 | 320_South_Boston_Building 1255 | Erasmus_University_Rotterdam 1256 | "1947-01-21"^^xsd:date 1257 | Florida_Panthers 1258 | "1983-10-03"^^xsd:date 1259 | Elizabeth_Garrett 1260 | Johns_Hopkins_University 1261 | John_Madin 1262 | Styria 1263 | "0-439-17684-0" 1264 | Bionico 1265 | Amdavad_ni_Gufa 1266 | 618 1267 | 250_Delaware_Avenue 1268 | "3048.0"^^xsd:double 1269 | Italy_national_under-17_football_team 1270 | Montevideo 1271 | Tapioca 1272 | "fried fish dumpling with tofu and vegetables inpeanut sauce" 1273 | Banjo 1274 | Europe 1275 | "Two door coupé"@en 1276 | Fuad_Masum 1277 | Siege_of_Petersburg 1278 | Ernie_Colón 1279 | "Petrol"@en 1280 | Houston 1281 | "1856-09-22"^^xsd:date 1282 | Andrew_Mitchell 1283 | Arrondissement_of_Antwerp 1284 | "14L/32R" 1285 | Jazz 1286 | German_language 1287 | Democratic_Labour_Party_(Brazil) 1288 | 4.862e+08 1289 | Prince_Edward_Island 1290 | "Warm (freshly baked) or cold" 1291 | "France, United States or China"@en 1292 | "AAIDBI"@en 1293 | Grantville_Gazette_II 1294 | "1987-02-27"^^xsd:date 1295 | 4.79343e+11 1296 | 2158 1297 | Walter_Baade 1298 | College_of_William_&_Mary 1299 | 2015_Campeonato_Brasileiro_Série_C 1300 | Balder_(comics) 1301 | "1923 OAA907 XC"@en 1302 | 1995 1303 | "1533.0"^^xsd:double 1304 | Tab_Benoit 1305 | Ethiopia 1306 | Chicago 1307 | Andreea_Bălan 1308 | Secretary_of_State_of_Vermont 1309 | "Nationwide in Indonesia, also popular in neighboring Southeast Asian countries"@en 1310 | "5.20906E8"^^ 1311 | Irlam_Town_F.C. 1312 | Patrick_McLoughlin 1313 | S.S._Robur_Siena 1314 | 16000 1315 | A_Wizard_of_Mars 1316 | Gruppo_Bertone 1317 | "/"@en 1318 | South_Jersey_Transportation_Authority 1319 | "1121.0"^^xsd:double 1320 | Germans_of_Romania 1321 | Honey 1322 | "4019.09"^^xsd:double 1323 | AIDAstella 1324 | Columbus_Blue_Jackets 1325 | Mendrisio 1326 | "1904"^^xsd:gYear 1327 | Frank_Borman 1328 | "62145.3"^^xsd:double 1329 | Philippines 1330 | Ace_Wilder 1331 | Round_Rock,_Texas 1332 | "Hot" 1333 | Squid 1334 | "Paris, New York or Hong Kong"@en 1335 | "1974-08-01"^^xsd:date 1336 | Akron_Summit_Assault 1337 | "140000.0"^^ 1338 | Lemon 1339 | "~500"@en 1340 | "1985" 1341 | Bhajji 1342 | "1.9304"^^xsd:double 1343 | Vladimir_Putin 1344 | 1996 1345 | "1981"^^xsd:gYear 1346 | Public_Square,_Cleveland 1347 | "ASB" 1348 | 1985 1349 | 1.83309e+08 1350 | Netherlands_national_under-17_football_team 1351 | "1995" 1352 | Station_wagon 1353 | "Asociación Deportiva"@en 1354 | Electronic_music 1355 | Abel_Hernández 1356 | Bakewell_tart 1357 | "336265"^^xsd:nonNegativeInteger 1358 | "Hugh Hazzard"@en 1359 | "Bhaji, bajji"@en 1360 | Lars_Løkke_Rasmussen 1361 | Peñarol 1362 | "320"^^xsd:positiveInteger 1363 | Japanese_occupation_of_British_Borneo 1364 | Aaron_Deer 1365 | "47487.6"^^xsd:double 1366 | "2014-07-13"^^xsd:date 1367 | Minna 1368 | Argentina 1369 | "1580.7"^^xsd:double 1370 | Asherton,_Texas 1371 | Club_sandwich 1372 | Colmore_Row 1373 | Oregon 1374 | Vermont 1375 | "1927" 1376 | "87000823" 1377 | McDonnell_Douglas_F-15_Eagle 1378 | Arts_and_Crafts_movement 1379 | Summit_County,_Ohio 1380 | ADO_Den_Haag 1381 | Fruits_de_Mer_Records 1382 | "2003"^^xsd:gYear 1383 | "compressed rice cooked in banana leaf with vegetables or minced meat fillings"@en 1384 | "Newport Pagnell, Buckinghamshire, England, United Kingdom"@en 1385 | Purple_Heart 1386 | Head_South_By_Weaving 1387 | Edmund_J._Davis 1388 | Invasion_of_Grenada 1389 | Airbus_Defence_and_Space 1390 | Greater_St._Louis 1391 | T._S._Thakur 1392 | Ícolo_e_Bengo 1393 | Netherlands 1394 | American_submarine_NR-1 1395 | Springfield,_Illinois 1396 | Paleobiology 1397 | Istanbul 1398 | Bobina 1399 | Adisham_Hall 1400 | Uruguayans 1401 | Operation_Enduring_Freedom 1402 | "1950"^^xsd:gYear 1403 | Roy_D._Chapin,_Jr. 1404 | "Universitas Apulensis"@en 1405 | British_Arabs 1406 | Acta_Mathematica_Hungarica 1407 | "210.0"^^xsd:double 1408 | Arrow_(comics) 1409 | "506.882"^^xsd:double 1410 | 512 1411 | Adolfo_Suárez_Madrid–Barajas_Airport 1412 | NRBQ 1413 | Asilomar_State_Beach 1414 | Aleksandr_Tukmanov 1415 | Autoharp 1416 | "57392246" 1417 | Rome 1418 | "Tomatoes, guanciale, cheese , olive oil"@en 1419 | Lansing,_Michigan 1420 | "Member of theHellenic Parliament" 1421 | Moscow 1422 | World_War_I 1423 | "non_performing_personnel" 1424 | "Secretary of State of Vermont" 1425 | "3.73513E8"^^ 1426 | West_Roxbury 1427 | Tadao_Ando 1428 | Prime_Minister_of_Romania 1429 | "22"^^xsd:positiveInteger 1430 | Fir_Park 1431 | "2.5977670247055E8"^^ 1432 | "4349.0"^^xsd:double 1433 | Azerbaijan_Premier_League 1434 | "AFIT, M.S. 1962" 1435 | 1988 1436 | Albert_Jennings_Fountain 1437 | Albany,_Georgia 1438 | 1._FC_Magdeburg 1439 | Bacon_Explosion 1440 | Rock_and_roll 1441 | "Technical Institute, Kaduna"@en 1442 | City_manager 1443 | Audi_A1 1444 | 1.85e+09 1445 | Bananaman 1446 | "2004-01-22"^^xsd:date 1447 | 1.35589e+08 1448 | "500.15"^^ 1449 | Ariane_5 1450 | Virtuti_Militari 1451 | "Samuda Brothers Cubitt Town, London"@en 1452 | Progressive_metal 1453 | "733.044"^^xsd:double 1454 | Democratic_Party_(United_States) 1455 | "86.4"^^xsd:double 1456 | University_of_Texas_at_Austin 1457 | "1200.0"^^xsd:double 1458 | George_Brenner 1459 | Convertible 1460 | "4.6"^^xsd:double 1461 | Sociology 1462 | "Edwin Eugene Aldrin Jr."@en 1463 | "Vice-President of New Democracy"@en 1464 | Four_World_Trade_Center 1465 | "0.0482"^^ 1466 | Socialist_Party_(Netherlands) 1467 | "1932-07-27"^^xsd:date 1468 | Alba 1469 | Blue 1470 | Derbyshire_Dales 1471 | "164.0"^^ 1472 | "2007-03-30"^^xsd:date 1473 | Putrajaya 1474 | 5 1475 | Errata,_Mississippi 1476 | Cleveland_City_Council 1477 | "10L/28R" 1478 | "VIAG" 1479 | "Medium expendable launch system"@en 1480 | "481248.0"^^xsd:double 1481 | Finland 1482 | Críona_Ní_Dhálaigh 1483 | "7900.0"^^xsd:double 1484 | Brazil_national_under-20_football_team 1485 | Adam_Koc 1486 | Alligator_Records 1487 | "76798317" 1488 | "Aurakles"@en 1489 | "2193.95"^^xsd:double 1490 | Al_Khor 1491 | Arrabbiata_sauce 1492 | Indian_Air_Force 1493 | White_people 1494 | Kevin_Eastman 1495 | Robert_A._Heinlein 1496 | 1960 1497 | Massimo_Drago 1498 | 51969173 1499 | Japanese_people 1500 | Brandon_Sanderson 1501 | Barack_Obama 1502 | Alfred_Giles_(architect) 1503 | Eric_Flint 1504 | Flanders 1505 | West_Germany 1506 | Sludge_metal 1507 | Claude_Bartolone 1508 | 6573 1509 | Alex_Day 1510 | Newport_Pagnell 1511 | "101 Ukrop Way"@en 1512 | Guadalajara 1513 | "1.337"^^xsd:double 1514 | Hungary 1515 | Mestizo 1516 | "Kuttikkattoor"@en 1517 | George_H._W._Bush 1518 | "Governor of Connecticut"@en 1519 | Rolls-Royce_Holdings 1520 | "1817.83"^^xsd:double 1521 | "2009-12-18"^^xsd:date 1522 | Addis_Ababa_City_Hall 1523 | "1046-8188" 1524 | Garth_Nix 1525 | "910.742"^^xsd:double 1526 | "2988.87"^^xsd:double 1527 | Eagle_Award_(comics) 1528 | VfL_Wolfsburg 1529 | Madeleine_L'Engle 1530 | Mindanao 1531 | Steuben_County,_Indiana 1532 | "Am. J. Math."@en 1533 | "SBCT" 1534 | "Tampere, Finland"@en 1535 | "170.0"^^xsd:double 1536 | "(Location of airport in Turkmenistan)" 1537 | Rashid_Rakhimov 1538 | Poaceae 1539 | "1988-11-22"^^xsd:date 1540 | "Barisan Ra'ayat Jati Sarawak"@en 1541 | 1862 1542 | "1588-2632" 1543 | Labour_Party_(Argentina) 1544 | Stanford_University 1545 | Belgium 1546 | "125800.0"^^ 1547 | "700"^^xsd:nonNegativeInteger 1548 | "AIP Adv."@en 1549 | "Marv Wolfman"@en 1550 | "AIT"@en 1551 | "265.0"^^xsd:double 1552 | Alternative_rock 1553 | "165.0"^^ 1554 | "India"@en 1555 | Avant-garde_metal 1556 | (410777)_2009_FD 1557 | Chicken 1558 | "Dodge Coronet"@en 1559 | Arion_(comics) 1560 | "3999.89"^^xsd:double 1561 | "4387.0"^^ 1562 | "4.8"^^ 1563 | Lewis_Nockalls_Cottingham 1564 | Monocotyledon 1565 | Gubbio 1566 | "Warm or cold"@en 1567 | Polydor_Records 1568 | "505, 575" 1569 | James_W._Reid_(politician) 1570 | Punk_rock 1571 | "57059226" 1572 | Vandenberg_AFB_Space_Launch_Complex_2 1573 | "Retired" 1574 | "1988" 1575 | Jesús_Casillas_Romero 1576 | 1_Decembrie_1918_University,_Alba_Iulia 1577 | "ORAA" 1578 | "Universitas Aarhusiensis"@en 1579 | Arepa 1580 | 11_Diagonal_Street 1581 | "Al Anbar Province, Iraq"@en 1582 | ACM_Transactions_on_Information_Systems 1583 | "53502"^^xsd:nonNegativeInteger 1584 | Mexico_City 1585 | "2002-12-05"^^xsd:date 1586 | AZAL_Arena 1587 | "Rome"@en 1588 | "A894 VA; A904 VD;"@en 1589 | "1558-2868" 1590 | Lee_County,_Alabama 1591 | 3678 1592 | "3800.86"^^xsd:double 1593 | W_Connection_F.C. 1594 | Austria-Hungary 1595 | "~173 K"@en 1596 | Gabriela_Michetti 1597 | 280 1598 | "1862" 1599 | "for Gravesham"@en 1600 | Spanish_Socialist_Workers'_Party 1601 | Metapán 1602 | Kate_Hardie 1603 | House_of_Low_Culture 1604 | Sri_Jayawardenepura_Kotte 1605 | "3800.0"^^xsd:double 1606 | Juventus_F.C. 1607 | Georgia_national_under-21_football_team 1608 | Susana_Mendoza 1609 | "January 2009"@en 1610 | 3.449e+08 1611 | 3180 1612 | The_Fylde 1613 | Pecorino_Romano 1614 | "Knightsbridge, London"@en 1615 | "47290"^^xsd:nonNegativeInteger 1616 | "89646863" 1617 | Conservative_Party_of_Canada 1618 | "167.94"^^xsd:double 1619 | Texas 1620 | Princeton_University 1621 | Washtenaw_County,_Michigan 1622 | Chrysler 1623 | Tony_Tan 1624 | "880000.0"^^xsd:double 1625 | Kendall_County,_Texas 1626 | Basim_Qasim 1627 | "APN"@en 1628 | Horacio_Rodríguez_Larreta 1629 | "6.7"^^ 1630 | British_Hong_Kong 1631 | "NWC, M.A. 1957" 1632 | "2011-08-27"^^xsd:date 1633 | Contra_Costa_County,_California 1634 | "Airbus Defence and Space for"@en 1635 | Audi_A1__4 1636 | Addiction 1637 | Arifin_Zakaria 1638 | Korean_War 1639 | Abraham_Lincoln 1640 | U.S._Castrovillari_Calcio 1641 | "Adams, Fall Creek, Lafayette, Richland, Union"@en 1642 | "1932-03-15"^^xsd:date 1643 | Malays_(ethnic_group) 1644 | "0-7653-0633-6" 1645 | "-71.0"^^ 1646 | Cephalopod_ink 1647 | 1955_Dodge__5 1648 | Acura_TLX 1649 | Mid-size_car 1650 | B._Zellner 1651 | Lippincott_Williams_&_Wilkins 1652 | Gordon_Marsden 1653 | Tony_Hall,_Baron_Hall_of_Birkenhead 1654 | Akeem_Ayers 1655 | "39371"^^xsd:nonNegativeInteger 1656 | "3090.0"^^xsd:double 1657 | 93645978 1658 | "1857-03-03"^^xsd:date 1659 | Ireland 1660 | Springer_Science+Business_Media 1661 | Dessert 1662 | Point_Fortin 1663 | 1987 1664 | Sulaiman_Abdul_Rahman_Taib 1665 | Pierce_County,_Washington 1666 | Rosids 1667 | Carpi_F.C._1909 1668 | Dick_Dillin 1669 | 200_Public_Square 1670 | Graz 1671 | Alexis_Tsipras 1672 | "202.0"^^ 1673 | Battle_of_Salem_Church 1674 | Fried_chicken 1675 | Essex_County,_New_York 1676 | NASA 1677 | Justicialist_Party 1678 | "13017.0"^^ 1679 | Delfino_Pescara_1936 1680 | Connecticut 1681 | "13.18"^^xsd:double 1682 | Siniša_Mihajlović 1683 | Cuyahoga_County,_Ohio 1684 | Americans 1685 | White_rice 1686 | "9953.28"^^xsd:double 1687 | "City Administrator"@en 1688 | Angola 1689 | Coventry 1690 | Catalonia 1691 | "600.151"^^xsd:double 1692 | Java 1693 | "93.8784"^^xsd:double 1694 | "9.8"^^ 1695 | Whiskey_Rebellion 1696 | Barrow_A.F.C. 1697 | Cornell_Big_Red 1698 | Albert_E._Austin 1699 | Ankara 1700 | "609.6"^^xsd:double 1701 | Bread 1702 | Italian_language 1703 | 2988 1704 | Scotland_national_under-19_football_team 1705 | Ann_Arbor,_Michigan 1706 | Anyang_Halla 1707 | AIP_Advances 1708 | "388"^^xsd:positiveInteger 1709 | Lega_Pro 1710 | "2005-11-26"^^xsd:date 1711 | T&TEC_Sports_Club 1712 | Post-metal 1713 | Maiden_Castle_(novel) 1714 | 19238 1715 | American_Motors 1716 | California_State_Assembly 1717 | Chesterfield_F.C. 1718 | "hot" 1719 | Chrysler_Newport 1720 | Mellow_Candle 1721 | Euro 1722 | Quattro_GmbH 1723 | Jorge_Humberto_Rodríguez 1724 | "Asser Levy Place and East 23rd Street"@en 1725 | Rona_Fairhead 1726 | Banyumasan_people 1727 | "33.8328"^^xsd:double 1728 | Virtus_Entella 1729 | "30 March 2007" 1730 | Boston_Bruins 1731 | Psychedelia 1732 | "Kelvin Mao"@en 1733 | "3990.0"^^xsd:double 1734 | "Rutland Arms, Bakewell, in 1820"@en 1735 | United_States_House_of_Representatives 1736 | Athens_International_Airport 1737 | Steve_Bright 1738 | "Punjab, Pakistan"@en 1739 | FC_Samtredia 1740 | Joe_Biden 1741 | Pietro_Canonica 1742 | "12R/30L" 1743 | 546 1744 | Orthoretrovirinae 1745 | Flowering_plant 1746 | "279.806"^^xsd:double 1747 | O_Canada 1748 | 45644811 1749 | Alan_Martin_(footballer,_born_1989) 1750 | "4100.0"^^xsd:double 1751 | Audi_A1__2 1752 | Faversham 1753 | Sergio_Mattarella 1754 | Æthelwald,_Ealdorman_of_East_Anglia 1755 | "211.0"^^xsd:double 1756 | Casey_Ribicoff 1757 | "Asphalt"@en 1758 | Peanut_sauce 1759 | 1959 1760 | Schiphol_Group 1761 | "50000"^^xsd:nonNegativeInteger 1762 | Baghdad 1763 | "Demak Jaya, Jalan Bako, Kuching, Sarawak"@en 1764 | "2013-03-17"^^xsd:date 1765 | Grozny 1766 | Brutalist_architecture 1767 | Amar_Osim 1768 | Cyrus_Vance,_Jr. 1769 | "359.64"^^xsd:double 1770 | Dave_Challinor 1771 | Romanesque_Revival_architecture 1772 | Afonso_Pena_International_Airport 1773 | Inkpot_Award 1774 | Voice_of_the_Wetlands_All-Stars 1775 | 4150 1776 | Qarabağ_FK 1777 | 7202 1778 | "Dr. G. P. Prabhukumar"@en 1779 | Potter_County,_Texas 1780 | Gulf_War 1781 | Steven_T._Seagle 1782 | Hearst_Castle 1783 | Four-stroke_engine 1784 | Dieter_Hecking 1785 | Historic_districts_in_the_United_States 1786 | 20 1787 | "Gram flour, vegetables"@en 1788 | Agra_Airport 1789 | "Governor of North Carolina" 1790 | "1989-02-24"^^xsd:date 1791 | FC_Rubin_Kazan 1792 | Front-engine_design 1793 | FC_Alania_Vladikavkaz 1794 | Bethesda,_Maryland 1795 | Kuching 1796 | Boeing_C-17_Globemaster_III 1797 | Carl_A._Wirtanen 1798 | "0.0"^^ 1799 | 2.83326e+11 1800 | Radboud_University_Nijmegen 1801 | 1955_Dodge__4 1802 | "4-speedautomatic(ZF 4HP18QE)" 1803 | John_van_den_Brom 1804 | Airey_Neave 1805 | Acehnese_people 1806 | Frederick_County,_Maryland 1807 | John_Geering 1808 | "Warton, Fylde, Lancashire"@en 1809 | Randall_County,_Texas 1810 | 23 1811 | "1971-07-01" 1812 | Alexandria,_Virginia 1813 | "*\n* at stern stabilizers."@en 1814 | 1080 1815 | "Aarhus, Denmark"@en 1816 | 2014–15_Eredivisie 1817 | Hong_Kong 1818 | "Member of the House of Representatives" 1819 | Norbert_Lammert 1820 | Madison_County,_Indiana 1821 | Uttar_Pradesh 1822 | "0.0925"^^ 1823 | Frederick,_Maryland 1824 | Coatbridge 1825 | "Nationwide in Indonesia, but more specific to Java"@en 1826 | Austrian_German 1827 | HAL_Light_Combat_Helicopter 1828 | "929.03"^^xsd:double 1829 | Scotland_national_under-21_football_team 1830 | 3.52497e+11 1831 | Eastern_Provincial_Council 1832 | Aaron_S._Daggett 1833 | Wilfred_R._Cousins,_Jr. 1834 | "64512.0"^^xsd:double 1835 | Olusegun_Obasanjo 1836 | Filipinos_in_Japan 1837 | Andrew_Rayel 1838 | Douglas_R._Oberhelman 1839 | Perth 1840 | "ASPHALT"@en 1841 | FC_Spartak_Moscow 1842 | "49805501" 1843 | Conservative_Party_(UK) 1844 | 20_Fenchurch_Street 1845 | "3746.66"^^ 1846 | Big_12_Conference 1847 | "0.0999"^^xsd:double 1848 | Alex_Tyus 1849 | "2"^^xsd:nonNegativeInteger 1850 | Gus_Poyet 1851 | Alfred_Garth_Jones 1852 | "1873.0"^^xsd:double 1853 | "3078.48"^^xsd:double 1854 | Maple_Ridge_Township,_Alpena_County,_Michigan 1855 | Attica,_Indiana 1856 | Manchester 1857 | Allan_Sandage 1858 | Irish_language 1859 | Carles_Puigdemont 1860 | Sumatra 1861 | Asian_Canadians 1862 | Lauraceae 1863 | Amharic 1864 | "27367194" 1865 | San_Sebastián_de_los_Reyes 1866 | F.C._Bari_1908 1867 | A.F.C._Blackpool 1868 | John_Sánchez 1869 | "1850.0"^^ 1870 | A_Fortress_of_Grey_Ice 1871 | Mondelez_International 1872 | Aarhus_University,_School_of_Business_and_Social_Sciences 1873 | "0-374-26131-8" 1874 | North_Wall,_Dublin 1875 | Staten_Island 1876 | Brown_sauce 1877 | 1.7e+06 1878 | Tirstrup 1879 | "2195.0"^^xsd:double 1880 | April_O'Neil 1881 | Andrews,_Texas 1882 | Dwight_D._Eisenhower 1883 | Sony_Music_Entertainment 1884 | Leon_Trotsky 1885 | Ahora_Madrid 1886 | Banana 1887 | Ryan_Potter 1888 | Asunción 1889 | Jan_Duursema 1890 | "Bryning Lane"@en 1891 | HOK_(firm) 1892 | 2014–15_Serie_A 1893 | "535.0"^^xsd:double 1894 | "December 31, 2006 (JD2454100.5)" 1895 | Miri,_Malaysia 1896 | 2014–15_Russian_Premier_League 1897 | "1875-03-04"^^xsd:date 1898 | "3.0"^^ 1899 | Oladipo_Diya 1900 | "3.9"^^ 1901 | 50 1902 | "63800.0"^^ 1903 | "Wiley-Blackwell on behalf of the Society for the Study of Addiction"@en 1904 | Real_Zaragoza 1905 | Shumai 1906 | "500.0"^^ 1907 | "60336.0"^^xsd:double 1908 | "518.16"^^xsd:double 1909 | "2009"^^xsd:gYear 1910 | "448"^^xsd:positiveInteger 1911 | "Texas"@en 1912 | Castle_(novel) 1913 | Dianne_Feinstein 1914 | Estádio_Municipal_Coaracy_da_Mata_Fonseca 1915 | 63 1916 | Portugal_national_football_team 1917 | "AFC Ajax NV"@en 1918 | "2215.0"^^xsd:double 1919 | Jefferson_Davis 1920 | "Massachusetts Institute of Technology, Sc.D. 1963"@en 1921 | Flemish_Region 1922 | 300_North_LaSalle 1923 | "ACM Trans. Inf. Syst."@en 1924 | "Wrecked" 1925 | Genoa_C.F.C. 1926 | "American"@en 1927 | Rosales 1928 | Indie_rock 1929 | 49805501 1930 | Singing 1931 | Aston_Martin 1932 | Palo_Seco_Velodrome 1933 | Jorge_Orosmán_da_Silva 1934 | Superleague_Greece 1935 | Daniel_Gould_Fowle 1936 | Bill_Everett 1937 | David_Scott 1938 | Austin,_Texas 1939 | "Edwin E. Aldrin, Jr." 1940 | 1963 1941 | "18.0"^^xsd:double 1942 | 57059226 1943 | FC_Dynamo_Moscow 1944 | "2776.73"^^xsd:double 1945 | Alvis_Speed_25__3 1946 | New_Mexico 1947 | "Armin Van Buuren, Bobina, Mark Sixma, Jonathan Mendelsohn, Christian Burns, Jwaydan, Alexander Popov, Jano, Alexandre Bergheau, Jonny Rose, Sylvia Tosun, Lira Yin, Alexandra Badoi"@en 1948 | Alvis_Car_and_Engineering_Company 1949 | "783.1"^^xsd:double 1950 | "Radboud University Nijmegen"@en 1951 | Alessio_Romagnoli 1952 | "Indonesia"@en 1953 | Noodle 1954 | Guanciale 1955 | "Flemish department of Mobility and Public Works"@en 1956 | "672"^^xsd:positiveInteger 1957 | Fort_Campbell 1958 | "1421.2"^^xsd:double 1959 | "2014.0"^^xsd:double 1960 | Kaliber_44 1961 | Acoustic_music 1962 | Newark,_New_Jersey 1963 | "Association Football Club Blackpool"@en 1964 | 75 1965 | "1927-07-23" 1966 | Greater_Los_Angeles_Area 1967 | 2009 1968 | Vila_Nova_Futebol_Clube 1969 | "A919 HA; 1927 WB;"@en 1970 | Helsinki 1971 | Francis_G._Slay 1972 | Viking_Press 1973 | "70634"^^xsd:nonNegativeInteger 1974 | Mason_School_of_Business 1975 | Massachusetts 1976 | "7/25" 1977 | Kourou,_French_Guiana 1978 | "1995-09-02"^^xsd:date 1979 | "20"^^xsd:positiveInteger 1980 | "MAT"@en 1981 | "East Link Bridge"@en 1982 | House_music 1983 | Puerto_Ricans 1984 | Battle_of_France 1985 | Rolando_Maran 1986 | Munster 1987 | Wellington 1988 | "Tomatoes,red chili,garlic,olive oil" 1989 | "December 2008"@en 1990 | 14th_New_Jersey_Volunteer_Infantry_Monument 1991 | Stockholm 1992 | Gram_flour 1993 | "AFIT, M.S. 1962"@en 1994 | Pakora 1995 | Sparta_Rotterdam 1996 | "AAIDBI" 1997 | Sergey_Belyavsky 1998 | "1239.35"^^xsd:double 1999 | Don_Sweeney 2000 | "90.72"^^xsd:double 2001 | "1.621640918388E8"^^ 2002 | Asher_and_Mary_Isabelle_Richardson_House 2003 | Agustín_Barboza 2004 | Blackpool 2005 | Albuquerque,_New_Mexico 2006 | Boris_Johnson 2007 | Efxeinoupoli 2008 | 83646315 2009 | John_F._Kennedy 2010 | Diemen 2011 | "June 1981" 2012 | Olympique_Lyonnais 2013 | Austria 2014 | "0-439-17685-9" 2015 | 6.81e+06 2016 | "12/30" 2017 | "→ Tom Tomsk"@en 2018 | Chameleon_Circuit_(band) 2019 | "3799.94"^^xsd:double 2020 | Williamsburg,_Virginia 2021 | Minangkabau_people 2022 | Folk_music 2023 | Ticino 2024 | Solanum 2025 | "16000"^^xsd:nonNegativeInteger 2026 | Diesel-electric_transmission 2027 | Erie_County,_New_York 2028 | "523.9"^^xsd:double 2029 | Albert_Uderzo 2030 | Kill_Rock_Stars 2031 | "~10000"@en 2032 | Al-Shamal_Sports_Club 2033 | Liberal_Democrats 2034 | Accrington_Stanley_F.C. 2035 | Berlin 2036 | "1976" 2037 | Ilocano_people 2038 | "1-4165-2060-0" 2039 | Bill_Marriott 2040 | Little_Chute,_Wisconsin 2041 | 600 2042 | Brooklyn 2043 | "16.86"^^ 2044 | "488160.0"^^xsd:double 2045 | "1958"^^xsd:gYear 2046 | "170.28"^^xsd:double 2047 | Minotaur_IV 2048 | "733.0"^^xsd:double 2049 | Singapore_dollar 2050 | Jean-Michel_Aulas 2051 | C._Woods 2052 | "202.15"^^ 2053 | "4.75426E8"^^ 2054 | "1965-01-03"^^xsd:date 2055 | Mendoza,_Argentina 2056 | "600"^^xsd:nonNegativeInteger 2057 | "Urban, ,\nIn Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090."@en 2058 | Kaiser_Chiefs 2059 | "Aarhus Lufthavn A/S"@en 2060 | Crewe_Alexandra_F.C. 2061 | "806" 2062 | "18C/36C 'Zwanenburgbaan'" 2063 | "3499.71"^^xsd:double 2064 | Left_Ecology_Freedom 2065 | Brian_Robertson_(trombonist) 2066 | National_Assembly_(Azerbaijan) 2067 | English_football_league_system 2068 | "24.0"^^xsd:double 2069 | Finnish_language 2070 | "Nurturing Excellence"@en 2071 | "November 26, 2005 (JD2453700.5)" 2072 | Atlantic_City,_New_Jersey 2073 | Warton,_Fylde 2074 | Nottingham 2075 | 3XN 2076 | FC_Sachsen_Leipzig 2077 | Aston_Martin_V8 2078 | Karl_Kesel 2079 | Lancashire 2080 | 1961 2081 | Abilene,_Texas 2082 | René_Goscinny 2083 | Spacewatch 2084 | Christian_Democratic_Appeal 2085 | American_Civil_War 2086 | Marysville_Auto_Plant 2087 | 657 2088 | Knightsbridge 2089 | Addis_Ababa_Stadium 2090 | "540000.0"^^xsd:double 2091 | Stadio_Dino_Manuzzi 2092 | Christian_Panucci 2093 | Red 2094 | Flemish_Government 2095 | Brasília 2096 | Bebi_Dol 2097 | "E11"@en 2098 | Walt_Disney_Studios_Motion_Pictures 2099 | "0.0068"^^xsd:double 2100 | Redefine_Properties_Limited 2101 | Chief_of_the_Astronaut_Office 2102 | "268.0"^^xsd:double 2103 | Gary_Cohn_(comics) 2104 | "Georgia"@en 2105 | Fulton_County,_Georgia 2106 | Pietro_Grasso 2107 | Greenville,_Wisconsin 2108 | Baymax 2109 | Rahm_Emanuel 2110 | 1964 2111 | Manuel_Valls 2112 | 1928 2113 | Alpharetta,_Georgia 2114 | Don_Tripp 2115 | "4099.86"^^xsd:double 2116 | Alfa_Romeo_164 2117 | Auron_(comics) 2118 | Byron_Brown 2119 | "UT Austin, B.S. 1955"@en 2120 | Tomato 2121 | "18R/36L 'Polderbaan'" 2122 | AZAL_PFK 2123 | John_Buscema 2124 | Wiley-Blackwell 2125 | "Governor of North Carolina"@en 2126 | Rabadash_Records 2127 | "4348.89"^^xsd:double 2128 | "17.92"^^ 2129 | Len_Wein 2130 | "12"^^xsd:positiveInteger 2131 | "1219.0"^^xsd:double 2132 | "0001-8392" 2133 | Turkmenabat_Airport 2134 | National_League_North 2135 | Chorizo 2136 | Don_Guardian 2137 | Josef_Klaus 2138 | Apium 2139 | "Ralph Payne"@en 2140 | A.T._Charlie_Johnson 2141 | Chinatown,_San_Francisco 2142 | Auckland 2143 | Dimmit_County,_Texas 2144 | "November 4, 2013" 2145 | Physics 2146 | Daniel_Martínez_(politician) 2147 | Grenadier_Guards 2148 | 13123 2149 | "140000.0"^^xsd:double 2150 | Rock_(geology) 2151 | Adams_County,_Pennsylvania 2152 | "22.86"^^xsd:double 2153 | Shehbaz_Sharif 2154 | Shinzō_Abe 2155 | 814 2156 | "Arts and Crafts Movement and American craftsman Bungalows"@en 2157 | U.C._Sampdoria 2158 | Anders_Osborne 2159 | "610.0"^^xsd:double 2160 | New_England_Patriots 2161 | Spanish_language 2162 | Mexicans 2163 | Bloomington,_Maryland 2164 | Phil_Lesh_and_Friends 2165 | Beef 2166 | 2014–15_Football_League_(Greece) 2167 | Chelsea_F.C. 2168 | Monocacy_National_Battlefield 2169 | "Sri Lanka"@en 2170 | 12 2171 | Groton,_Connecticut 2172 | Damon_Wayans,_Jr. 2173 | Vegetable 2174 | 1036_Ganymed 2175 | Ithaca,_New_York 2176 | Solanaceae 2177 | Canada 2178 | Gujarat 2179 | New_Douglas_Park 2180 | Ducati 2181 | Sedan_(automobile) 2182 | A_EPSTH_2nd_GROUP 2183 | Pork 2184 | 45 2185 | Dutch_language 2186 | BLT 2187 | "28.0"^^xsd:double 2188 | Wee_Giant 2189 | "1983" 2190 | "ACM Trans. Inf. Syst." 2191 | Sycamore,_Illinois 2192 | 1.42e+07 2193 | "Legion of Merit ribbon.svg"@en 2194 | Paracuellos_de_Jarama 2195 | 9001 2196 | "74.3326587666432"^^ 2197 | "United States representatives"@en 2198 | Athens 2199 | 5300 2200 | Akihito 2201 | Leinster 2202 | Alfred_N._Phillips 2203 | Cumberland_County,_Pennsylvania 2204 | Cuttlefish 2205 | Alhambra_(1855) 2206 | "Faversham, Kent, England"@en 2207 | "1.55"^^xsd:double 2208 | Eagle_(automobile) 2209 | 1480153 2210 | Commelinids 2211 | Indonesia 2212 | Diane_Duane 2213 | Alex_Plante 2214 | "16/34" 2215 | Weymouth_Sands 2216 | Harcourt_(publisher) 2217 | "Jon Valor"@en 2218 | Al-Taqaddum_Air_Base 2219 | Ska_punk 2220 | "173.0"^^ 2221 | Shrimp 2222 | Bird 2223 | Baku 2224 | 2.52783e+06 2225 | Indonesian_language 2226 | James_Pallotta 2227 | "973.0"^^xsd:double 2228 | Atlanta_City_Council 2229 | Black_Pirate 2230 | New_wave_music 2231 | Alagoas 2232 | Congress_Poland 2233 | "164.0"^^xsd:double 2234 | SK_Vorwärts_Steyr 2235 | Stadio_Armando_Picchi 2236 | "AMAHE9"@en 2237 | AIDAluna 2238 | Gravesend 2239 | Ardmore_Airport_(New_Zealand) 2240 | Audi_e-tron 2241 | 3800 2242 | "9-speedZF9HPautomatic(V6)" 2243 | AFC_Ajax 2244 | Marietta,_Ohio 2245 | Isis_(band) 2246 | "Dessert"@en 2247 | Gábor_Kubatov 2248 | "19238"^^xsd:nonNegativeInteger 2249 | Egg_Harbor_Township,_New_Jersey 2250 | Alexander_Nouri 2251 | 3.77016e+11 2252 | Batchoy 2253 | "Governor of West Virginia"@en 2254 | "4.79343E8"^^ 2255 | "2900.0"^^xsd:double 2256 | Hogao 2257 | African_Americans 2258 | Abradab 2259 | "161.53755844165633"^^ 2260 | Apollo_11 2261 | Owen_Glendower_(novel) 2262 | "8.334"^^xsd:double 2263 | Kristina_Kovač 2264 | "Provisional President of the Argentine Senate" 2265 | Sapindales 2266 | "210.922"^^xsd:double 2267 | City_of_Brussels 2268 | Russian_Civil_War 2269 | University_of_Vienna 2270 | George_Kapitan 2271 | Stellendam 2272 | 1.07433e+08 2273 | Greymachine 2274 | "Minister of Transport" 2275 | Caterpillar_Inc. 2276 | Austria_national_football_team 2277 | 4.19113e+11 2278 | Benton_County,_Oregon 2279 | Los_Angeles_Herald-Examiner 2280 | "507.0"^^xsd:double 2281 | "94.8024"^^ 2282 | "755.3"^^xsd:double 2283 | Harvard_University 2284 | "560"^^xsd:positiveInteger 2285 | Mexico 2286 | Spaniards 2287 | Great_Britain 2288 | Kuala_Lumpur 2289 | "280.0"^^xsd:double 2290 | United_States_Army 2291 | Steve_Bruce 2292 | Indianapolis 2293 | "5.11592E8"^^ 2294 | Belgaum 2295 | "1878"^^xsd:gYear 2296 | Faroese_language 2297 | "765" 2298 | Denzil_Antonio 2299 | "Member of the Texas State Senate from District 4"@en 2300 | Lafayette_Township,_Madison_County,_Indiana 2301 | Deșteaptă-te,_române! 2302 | US_Lesquin 2303 | Philips_Records 2304 | A.C._Milan 2305 | 76798317 2306 | Battle_of_Cold_Harbor 2307 | "1989-07-10"^^xsd:date 2308 | Jacob_Bundsgaard 2309 | Lake_Placid,_New_York 2310 | Mols 2311 | Ellington,_Wisconsin 2312 | Carnival_Corporation_&_plc 2313 | Tarrant_County,_Texas 2314 | Apollo_12 2315 | Avocado 2316 | Chris_Patten 2317 | "Mayor"@en 2318 | "1988-01-08"^^xsd:date 2319 | Abarth_1000_GT_Coupé__1 2320 | "New Mexico"@en 2321 | "4019.0"^^xsd:double 2322 | "140.8"^^ 2323 | "03R/21L" 2324 | Enrique_Peña_Nieto 2325 | Meyer_Werft 2326 | Musician 2327 | Stockport_County_F.C. 2328 | "Member of the Congress of Deputies"@en 2329 | National_Park_Service 2330 | The_Violet_Keystone 2331 | "Helipad" 2332 | Farrar,_Straus_and_Giroux 2333 | Buffalo,_New_York 2334 | "solo_singer" 2335 | California 2336 | "23"^^xsd:positiveInteger 2337 | "60040714" 2338 | Halimah_Yacob 2339 | Richland_Township,_Madison_County,_Indiana 2340 | 476 2341 | Bajik 2342 | MTU_Friedrichshafen 2343 | "17L/35R" 2344 | Texan 2345 | Alfred_Worden 2346 | Shortcrust_pastry 2347 | "0025-5858" 2348 | James_Craig_Watson 2349 | Addiction_(journal) 2350 | "1865-8784" 2351 | August_Horch 2352 | "1979-03-30"^^xsd:date 2353 | Arapiraca 2354 | The_Bodley_Head 2355 | Grand_Chute,_Wisconsin 2356 | "34"^^xsd:positiveInteger 2357 | "1793-10-23"^^xsd:date 2358 | Tennis 2359 | Audi_Brussels 2360 | Richard_J._Berry 2361 | Adenan_Satem 2362 | "*\n*Box keel depth :"@en 2363 | "SEAT Ibiza Mk4"@en 2364 | Budapest 2365 | Birmingham_City_Council 2366 | Alan_Shepard 2367 | "Print & Paperback"@en 2368 | "Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas"@en 2369 | Favoritner_AC 2370 | "\"Squeezed\" or \"smashed\"fried chickenserved withsambal" 2371 | "3992.88"^^xsd:double 2372 | 53502 2373 | Aleksandr_Chumakov 2374 | "1174"^^xsd:positiveInteger 2375 | Monroe_Township,_Madison_County,_Indiana 2376 | Michigan 2377 | White_South_African 2378 | Sweet_potato 2379 | A.D._Isidro_Metapán 2380 | "3360.12"^^xsd:double 2381 | Jwaydan_Moyine 2382 | Luanda_Province 2383 | 3Arena 2384 | 3._Liga 2385 | RCA_Records 2386 | Lazio 2387 | Barny_Cakes 2388 | Menasha_(town),_Wisconsin 2389 | "17023"^^xsd:nonNegativeInteger 2390 | Above_the_Veil 2391 | Union_Army 2392 | AIDS_(journal) 2393 | Asterix_(character) 2394 | Juha_Sipilä 2395 | 318875313 2396 | "0002-9327" 2397 | "(original structure); 1832 (current structure)" 2398 | "Astérix"@en 2399 | "(Location of airport in India)" 2400 | Windsor,_Connecticut 2401 | Wolters_Kluwer 2402 | Imst 2403 | Costa_Crociere 2404 | Tuanku_Bujang_Tuanku_Othman 2405 | Static_Caravan_Recordings 2406 | Buzz_Aldrin 2407 | "1929" 2408 | Blockbuster_(DC_Comics) 2409 | John_Clancy_(Labour_politician) 2410 | Pallacanestro_Cantù 2411 | Luis_Miguel_Ramis 2412 | Ben_Ramsey 2413 | "100305.0"(minutes) 2414 | Francisco_Franco 2415 | Schenectady,_New_York 2416 | "NWC, M.A. 1957"@en 2417 | Copenhagen 2418 | Abarth_1000_GT_Coupé 2419 | 108_St_Georges_Terrace 2420 | "Asa Gigante ''"@en 2421 | 1.62447e+07 2422 | Marius_Moga 2423 | "2195.17"^^xsd:double 2424 | Fallujah 2425 | Delta_II 2426 | "Kellamergh Park"@en 2427 | Luciano_Spalletti 2428 | Zamboangans 2429 | Câmpia_Turzii 2430 | Montreal_Locomotive_Works 2431 | Serie_B 2432 | 8805735 2433 | AFAS_Stadion 2434 | Galactic 2435 | Serie_A 2436 | "14/32" 2437 | Virginia 2438 | "12L/30R" 2439 | "1933-10-17" 2440 | RB_Leipzig 2441 | "January, 2014" 2442 | William_M._O._Dawson 2443 | "1.8"^^ 2444 | "1978-11-12"^^xsd:date 2445 | Newmarket,_Ontario 2446 | Hugo_Sperrle 2447 | "2015-06-27"^^xsd:date 2448 | Paleontology 2449 | Vicenza_Calcio 2450 | "39500.0"^^ 2451 | ARA_Veinticinco_de_Mayo_(V-2) 2452 | Agra 2453 | "38.892"^^xsd:double 2454 | Haputale 2455 | Aston_Martin_Virage 2456 | 4.16136e+11 2457 | Robert_Eenhoorn 2458 | George_Richard_Pain 2459 | Antwerp_International_Airport 2460 | Allen_Forrest 2461 | Varese_Calcio_S.S.D. 2462 | Drone_music 2463 | Chennai 2464 | Clayton,_Winnebago_County,_Wisconsin 2465 | Ethiopian_birr 2466 | Sanat_Mes_Kerman_F.C. 2467 | 1931 2468 | Blues 2469 | United_States 2470 | 2939 2471 | "46451790" 2472 | 10_Hygiea 2473 | Audi 2474 | California_State_Senate 2475 | Meride 2476 | Uruguay_Olympic_football_team 2477 | "22.1"^^xsd:double 2478 | University_of_Michigan 2479 | Real_Madrid_Castilla 2480 | Sportpark_De_Toekomst 2481 | Songwriter 2482 | Stanyan_Records 2483 | "Associazione Sportiva"@en 2484 | "524.5"^^ 2485 | "1982-07-23"^^xsd:date 2486 | ","@en 2487 | "In service" 2488 | "1981" 2489 | Alpena,_Michigan 2490 | "~179 K"@en 2491 | Cruise_ship 2492 | Rostock 2493 | Agremiação_Sportiva_Arapiraquense 2494 | "1891-05-20"^^xsd:date 2495 | Lockheed_Martin 2496 | "Ajo blanco"@en 2497 | Electroacoustic_music 2498 | Wichita_Thunder 2499 | "ATISET" 2500 | Buenos_Aires 2501 | Georgetown,_Texas 2502 | Trance_music 2503 | Battle_of_Chancellorsville 2504 | Ska 2505 | Binignit 2506 | European_University_Association 2507 | "ATW"@en 2508 | "AMHAAJ" 2509 | Fruit_preserves 2510 | Juan_Carlos_I_of_Spain 2511 | Macmillan_Publishers 2512 | Visvesvaraya_Technological_University 2513 | "3180"^^xsd:nonNegativeInteger 2514 | Condensed_milk 2515 | Blaenau_Ffestiniog 2516 | "1910" 2517 | Stephen_Yong_Kuet_Tze 2518 | Buenos_Aires_City_Legislature 2519 | Russia_national_football_B_team 2520 | 1634:_The_Bavarian_Crisis 2521 | Fountain_County,_Indiana 2522 | 106 2523 | 8.37081e+11 2524 | Shanachie_Records 2525 | "Tomatoes,guanciale, cheese (Pecorino Romano),olive oil" 2526 | Dan_Mishkin 2527 | Greenlandic_language 2528 | 8.78885e+09 2529 | 1001_Gaussia 2530 | "253.26"^^xsd:double 2531 | Denmark 2532 | Alfa_Romeo_164__5 2533 | Sara_Miller_McCune 2534 | Peter_Stöger 2535 | Appleton,_Wisconsin 2536 | Gregory_L._Fenves 2537 | Andra_(singer) 2538 | President_of_the_United_States 2539 | De_Graafschap 2540 | Milk 2541 | White_Americans 2542 | 1089_Tama 2543 | "1936-05-21"^^xsd:date 2544 | "Deputy Parliamentary Spokesman of Popular Orthodox Rally"@en 2545 | Abel_Caballero 2546 | Narendra_Modi 2547 | Michele_Marcolini 2548 | Polish_Academy_of_Sciences 2549 | Dougherty_County,_Georgia 2550 | "2013-09-18"^^xsd:date 2551 | Mid-Atlantic_Regional_Spaceport 2552 | 2014 2553 | Seat_of_local_government 2554 | "476"^^xsd:nonNegativeInteger 2555 | 1911 2556 | Hergé 2557 | Al_Shorta_SC 2558 | Fiat_Croma 2559 | "1930-01-20" 2560 | Javanese_people 2561 | 70634 2562 | "Offices:"@en 2563 | 12467 2564 | Lincoln,_Nebraska 2565 | Hoofdklasse 2566 | Amazing-Man_(Centaur_Publications) 2567 | Marv_Wolfman 2568 | SAGE_Publications 2569 | Ballistic_(DC_Comics) 2570 | "2005-08-11"^^xsd:date 2571 | Qatar_Stars_League 2572 | United_Petrotrin_F.C. 2573 | LASK_Linz 2574 | 50000 2575 | Condiment 2576 | Enda_Kenny 2577 | Stan_Lee 2578 | "3.04"^^xsd:double 2579 | "8805735" 2580 | 2581 | "Colmore Row, Birmingham, England"@en 2582 | "2013-03-16"^^xsd:date 2583 | "Main course"@en 2584 | Antonis_Milionis 2585 | "red beans,pork belly, whiterice,ground meat,chicharon,fried egg,plantain(patacones),chorizo, arepa, hogao sauce,black pudding(morcilla),avocadoandlemon" 2586 | "Massachusetts Institute of Technology, Sc.D. 1963" 2587 | "1799.84"^^xsd:double 2588 | New_Netherland 2589 | Bernalillo_County,_New_Mexico 2590 | Alpena_County,_Michigan 2591 | Eisner_Award 2592 | Rosaceae 2593 | Tor_Books 2594 | Giorgos_Kaminis 2595 | "914.8"^^xsd:double 2596 | Corvallis,_Oregon 2597 | 1._FC_Lokomotive_Leipzig 2598 | Neptun_Werft 2599 | "6.11961E8"^^ 2600 | English_language 2601 | German_Empire 2602 | Bart_De_Wever 2603 | Kashubians 2604 | Tim_Brooke-Taylor 2605 | Carrie_Lam_(politician) 2606 | California_State_Capitol 2607 | Oregano 2608 | "1937"^^xsd:gYear 2609 | Sri_Lanka 2610 | 1000_Piazzia 2611 | Smilodon 2612 | 2015 2613 | Kids_Imagine_Nation 2614 | Maccabi_Tel_Aviv_F.C. 2615 | 46451790 2616 | "2194.0"^^xsd:double 2617 | 39371 2618 | "Addiction" 2619 | Mario_Botta 2620 | Wheel_Horse 2621 | 2000 2622 | Drogheda_United_F.C. 2623 | "Abh. Math. Semin. Univ. Hambg."@en 2624 | "0269-9370" 2625 | David_Cameron 2626 | "Lega Pro/A"@en 2627 | "42.0"^^xsd:double 2628 | Cauliflower 2629 | Ganges 2630 | Germany 2631 | Felipe_VI_of_Spain 2632 | S.S._Chieti_Calcio 2633 | Turkey 2634 | "ADICE5" 2635 | "7500.0"^^xsd:double 2636 | "2005-04-07"^^xsd:date 2637 | Paisa_Region 2638 | Siomay 2639 | American_Institute_of_Physics 2640 | Chinese_people_in_Japan 2641 | Maine 2642 | "single plate clutch, separate 4-speed gearbox all-silent" 2643 | "5.23329E8"^^ 2644 | University_of_Adelaide 2645 | "7.5"^^ 2646 | Lemper 2647 | "597.408"^^xsd:double 2648 | Alba_Iulia 2649 | Anwar_Zaheer_Jamali 2650 | Haider_al-Abadi 2651 | Zamba_(artform) 2652 | Covington,_Indiana 2653 | Bibbo_Bibbowski 2654 | Saranac_Lake,_New_York 2655 | "2014-01-09"^^xsd:date 2656 | Parti_Pesaka_Bumiputera_Bersatu 2657 | Tudor_Revival_architecture 2658 | ALCO_RS-3 2659 | "APGPAC"@en 2660 | Black_pudding 2661 | CRBL 2662 | Test_pilot 2663 | "Bacon,sausage" 2664 | "São José dos Pinhais, Brazil"@en 2665 | Maccabi_Ashdod_B.C. 2666 | Accademia_di_Architettura_di_Mendrisio 2667 | "5.4"^^ 2668 | "Tudor and Jacabian"@en 2669 | "429.9"^^xsd:double 2670 | Angola,_Indiana 2671 | Johann_Schneider-Ammann 2672 | "1532.53"^^xsd:double 2673 | David_Weber 2674 | "78771100" 2675 | Akeem_Priestley 2676 | 541458 2677 | "Association Football Club Fylde"@en 2678 | 1.39705e+08 2679 | A.E_Dimitra_Efxeinoupolis 2680 | "Rick Parker"@en 2681 | "1218.59"^^xsd:double 2682 | Vermicelli 2683 | "600.0"^^xsd:double 2684 | Johns_Hopkins_University_Press 2685 | Andalusia 2686 | Atlanta 2687 | Olympic_Stadium_(Athens) 2688 | "fried fish dumpling with tofu and vegetables in peanut sauce"@en 2689 | "3000.0"^^xsd:double 2690 | Adam_Maher 2691 | SEAT_Ibiza 2692 | J._V._Jones 2693 | Jalisco 2694 | "non_performing_personnel"@en 2695 | 1.03926e+08 2696 | Alabama 2697 | "Acta Palaeontol. Pol." 2698 | Cape_Town 2699 | "6700.0"^^xsd:double 2700 | Ice_cream 2701 | P&O_(company) 2702 | Sergey_Rodionov 2703 | Chinese_Filipino 2704 | Debbie_Wasserman_Schultz 2705 | University_of_Texas_System 2706 | Chief_of_the_Defence_Staff_(Nigeria) 2707 | Citrus 2708 | "Max Huiberts"@en 2709 | Acharya_Institute_of_Technology 2710 | "518.0"^^xsd:double 2711 | Philip_Charles_Hardwick 2712 | Virginia_DeMarce 2713 | "May 1950 – August 1956"@en 2714 | Ben_Urich 2715 | A._Storrs 2716 | "1930-01-20"^^xsd:date 2717 | "Livorno Calcio S.p.A."@en 2718 | "173.0"^^xsd:double 2719 | Funk 2720 | "4.40756E8"^^ 2721 | A.S._Gubbio_1910 2722 | Meringue 2723 | "''Alvinegro"@en 2724 | Whig_Party_(United_States) 2725 | Paraná_(state) 2726 | Asajaya 2727 | Robert_A._M._Stern 2728 | 101_Helena 2729 | Gardner_Fox 2730 | Padang_cuisine 2731 | Alcatraz_Versus_the_Evil_Librarians 2732 | Distinguished_Service_Medal_(United_States_Navy) 2733 | Uruguay_national_football_team 2734 | AFC_Ajax_(amateurs) 2735 | "Blue, White and Orange" 2736 | "Exchange National Bank Building"@en 2737 | "Institute of Paleobiology, Polish Academy of Sciences"@en 2738 | "2000"^^xsd:gYear 2739 | "House of Representatives (Netherlands)"@en 2740 | King_County,_Washington 2741 | Eberhard_van_der_Laan 2742 | "253260.0"^^ 2743 | Empoli_F.C. 2744 | Milan 2745 | Oleh_Luzhny 2746 | "5.6"^^ 2747 | 18 2748 | Persea 2749 | London 2750 | Baku_Turkish_Martyrs'_Memorial 2751 | Salvadoran_Primera_División 2752 | Palm_sugar 2753 | B_postcode_area 2754 | Akron,_Ohio 2755 | Charles_Michel 2756 | Antares_(rocket) 2757 | Williamson_County,_Texas 2758 | "1104.1"^^xsd:double 2759 | "AMHAAJ"@en 2760 | St._Vincent–St._Mary_High_School 2761 | "1966-02-28"^^xsd:date 2762 | Grantville_Gazette_III 2763 | Johnny_Sansone 2764 | "White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers"@en 2765 | Agnes_Kant 2766 | K2_(Kovač_sisters_duo) 2767 | Inter_Milan 2768 | Antonis_Samaras 2769 | Kempe_Gowda_I 2770 | "27250"^^xsd:nonNegativeInteger 2771 | "09R/27L" 2772 | Kerala 2773 | Bakso 2774 | Samuda_Brothers 2775 | General_Dynamics 2776 | 1.30786e+08 2777 | Koreans_in_Japan 2778 | Roger_Stern 2779 | Cello 2780 | "A900 GA"@en 2781 | São_José_dos_Pinhais 2782 | Jill_Shilling 2783 | Front-engine,_front-wheel-drive_layout 2784 | "159.0"^^xsd:double 2785 | "1982.0"^^xsd:double 2786 | Philippine_Spanish 2787 | Awadh 2788 | "12.0"^^xsd:double 2789 | Raúl_Fernando_Sendic_Rodríguez 2790 | "1480153" 2791 | Joko_Widodo 2792 | 17000 2793 | Lahore 2794 | FC_Admira_Wacker_Mödling 2795 | Peoria,_Illinois 2796 | Los_Angeles_Rams 2797 | "Haputale, Sri Lanka"@en 2798 | Klaus_Iohannis 2799 | "John Aman"@en 2800 | Oyster_sauce 2801 | Indiana 2802 | Association_of_Public_and_Land-grant_Universities 2803 | Pacific_Grove,_California 2804 | "Addiction"@en 2805 | "Spacewatch"@en 2806 | Church_of_England 2807 | California's_11th_State_Assembly_district 2808 | Uruguayan_Segunda_División 2809 | Department_of_Commerce_Gold_Medal 2810 | 11th_Mississippi_Infantry_Monument 2811 | A.S.D._S.S._Nola_1925 2812 | "July 14, 2004 (JD2453200.5)" 2813 | Harry_Sahle 2814 | "170.0"^^ 2815 | Amsterdam 2816 | Alley_Award 2817 | "April 2014"@en 2818 | Outagamie_County,_Wisconsin 2819 | Stuart_Parker_(footballer) 2820 | Switzerland 2821 | Johannesburg 2822 | 22 2823 | Cake 2824 | Joachim_Gauck 2825 | "51969173" 2826 | Akita,_Akita 2827 | Hamilton_Academical_F.C. 2828 | Australians 2829 | "United States Secretary of Health, Education, and Welfare" 2830 | Straight-six_engine 2831 | "90640840" 2832 | "1.8"^^xsd:double 2833 | All_India_Council_for_Technical_Education 2834 | "16.76"^^ 2835 | "7.5E7"^^ 2836 | San_Francisco 2837 | Doña_Ana_County,_New_Mexico 2838 | Mayor_of_Albuquerque 2839 | "1"^^xsd:nonNegativeInteger 2840 | "202.0"^^xsd:double 2841 | Robert_E._Lee 2842 | "1969-09-01" 2843 | "3571.0"^^ 2844 | Rhythm_and_blues 2845 | Andreas_Voßkuhle 2846 | "2743.5"^^xsd:double 2847 | Anandiben_Patel 2848 | Asilomar_Conference_Grounds 2849 | "Abh. Math. Semin. Univ. Hambg." 2850 | "Fish cooked in sour and hot sauce"@en 2851 | AZ_Alkmaar 2852 | Antioch,_California 2853 | British_people 2854 | Afrobeat 2855 | B-Unique_Records 2856 | Albennie_Jones 2857 | Saab_9000 2858 | Live_Nation_Entertainment 2859 | Infraero 2860 | "8820.0"^^ 2861 | Kochi 2862 | Azerbaijan 2863 | Walt_Simonson 2864 | Buxton 2865 | Water 2866 | "ASCQAG" 2867 | Taylor_County,_Texas 2868 | "Kheria Air Force Station"@en 2869 | Association_of_MBAs 2870 | Colombia 2871 | Adolf_Schärf 2872 | Puya_(singer) 2873 | "1853-03-04"^^xsd:date 2874 | Qiu_Xiaolong 2875 | 8.3e+06 2876 | 2014–15_Football_Conference 2877 | Honda_K_engine 2878 | Flibbertigibbet 2879 | 325 2880 | Laura_Boldrini 2881 | "1998"^^xsd:gYear 2882 | "17000"^^xsd:nonNegativeInteger 2883 | "1953-06-30"^^xsd:date 2884 | 47290 2885 | "60696.0"^^xsd:double 2886 | Ambient_music 2887 | Burlington,_Vermont 2888 | Franklin_D._Roosevelt 2889 | "1510.0"^^xsd:double 2890 | Mayors_of_Atlantic_City,_New_Jersey 2891 | 1913 2892 | "497.129"^^xsd:double 2893 | 109_Felicitas 2894 | "Member of the"@en 2895 | Calcio_Catania 2896 | Asphalt 2897 | "992.6"^^xsd:double 2898 | 4.45895e+11 2899 | Rutaceae 2900 | "Governor of Connecticut" 2901 | 1986_United_States_bombing_of_Libya 2902 | Charlie_McDonnell 2903 | 2953 2904 | "Richard J. Berry"@en 2905 | Soviet_Union 2906 | Mamiffer 2907 | Atalanta_B.C. 2908 | 1500 2909 | Battle_of_Antietam 2910 | Guiana_Space_Centre 2911 | "1941"^^xsd:gYear 2912 | "December 2008" 2913 | Alan_B._Miller_Hall 2914 | KV_Mechelen 2915 | List_of_Provisional_Presidents_of_Argentine_Senate 2916 | "Livorno, Italy"@en 2917 | Sesame_oil 2918 | Appleton_International_Airport 2919 | "Lalbhai Dalpatbhai Campus, nearCEPT University, opp.Gujarat University, University Road"@en 2920 | Turkish_Basketball_Super_League 2921 | Peritonitis 2922 | Bianca_Castafiore 2923 | Amsterdam_Airport_Schiphol 2924 | San_Marcos,_Texas 2925 | "24.48"^^xsd:double 2926 | 23900 2927 | Aktieselskab 2928 | "404/678/470" 2929 | Atiku_Abubakar 2930 | "United States"@en 2931 | Management 2932 | Goslarer_SC_08 2933 | "1800.0"^^xsd:double 2934 | Russia 2935 | "Deceased"@en 2936 | "Verona, Italy"@en 2937 | Amsterdam-Noord 2938 | Amatriciana_sauce 2939 | Mark_Rutte 2940 | "40.744"^^xsd:double 2941 | Aleksey_Chirikov_(icebreaker) 2942 | Tom_Tait 2943 | HIV/AIDS 2944 | Uab 2945 | Vitesse_Arnhem 2946 | "City Manager"@en 2947 | "1142.3"^^ 2948 | "1.524"^^ 2949 | String_instrument 2950 | New_Mexico_Senate 2951 | E-book 2952 | Doesburg 2953 | "Virginia DeMarce and"@en 2954 | Politician 2955 | Ground_meat 2956 | Contributing_property 2957 | Gettysburg,_Pennsylvania 2958 | Moro_people 2959 | "1411.22"^^xsd:double 2960 | "03/21" 2961 | World_War_II 2962 | "84.0"^^xsd:double 2963 | India 2964 | 2014–15_Serie_B 2965 | Kidney_bean 2966 | Kenosha,_Wisconsin 2967 | "42 m"@en 2968 | "09/27 'Buitenveldertbaan'" 2969 | Sausage 2970 | AEK_Athens_F.C. 2971 | John_Cowper_Powys 2972 | "18R/36L" 2973 | John_Reith,_1st_Baron_Reith 2974 | "88002539" 2975 | 1.42603e+08 2976 | Kirk_Joseph 2977 | "1986-04-15"^^xsd:date 2978 | "37.16"^^ 2979 | "5300"^^xsd:nonNegativeInteger 2980 | Amsterdam-Centrum 2981 | Silvio_Berlusconi 2982 | Carmine_Infantino 2983 | Halton_Arp 2984 | Strawberry 2985 | Akita_Museum_of_Art 2986 | "→ Anzhi Makhachkala"@en 2987 | Alfa_Romeo_164__4 2988 | Konstantinos_Mitsotakis 2989 | Ring_of_Fire_II 2990 | "single plate clutch, separate 4-speed gearbox all-silent and \n\nall-syncromesh, centre change lever, open tubular propellor\n shaft with metal joints , spiral bevel fully floating back axle"@en 2991 | University_of_Göttingen 2992 | François_Hollande 2993 | Stadio_Olimpico 2994 | 89646863 2995 | Colin_Powell 2996 | "Sumatra and Malay Peninsula"@en 2997 | Frank_G._Jackson 2998 | AMC_Matador 2999 | "Asilomar Blvd., Pacific Grove, California"@en 3000 | (66063)_1998_RO1 3001 | Atlantic_County,_New_Jersey 3002 | Beverly_Hills,_California 3003 | Country_music 3004 | "Asam padeh"@en 3005 | Bella_Center 3006 | "34.0"^^xsd:double 3007 | Polish–Soviet_War 3008 | "1986"^^xsd:gYear 3009 | "1905-03-04"^^xsd:date 3010 | "1969-01-25"^^xsd:date 3011 | Cookie 3012 | Republican_Party_(United_States) 3013 | "2013-09-28"^^xsd:date 3014 | "Parti Bumiputera Sarawak"@en 3015 | 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neighboringMunicipality POPULATEDPLACE POPULATEDPLACE 104 | first * * 105 | r1LengthM * * 106 | aircraftAttack MILITARYUNIT MEANOFTRANSPORTATION 107 | escapeVelocity PLANET HEMA#DOUBLE 108 | completionDate WORK HEMA#DATE 109 | designer * PERSON 110 | fate COMPANY -RDF-SYNTAX-NS#LANGSTRING 111 | free FICTIONALCHARACTER HEMA#STRING 112 | dedicatedTo * * 113 | postalCode * HEMA#STRING 114 | foundingDate * HEMA#DATE 115 | precededBy * * 116 | union * * 117 | isPartOf * * 118 | distributingCompany RECORDLABEL COMPANY 119 | activeYearsEndYear * HEMA#GYEAR 120 | open * * 121 | carbohydrate FOOD HEMA#DOUBLE 122 | officialLanguage POPULATEDPLACE LANGUAGE 123 | owningOrganisation * ORGANISATION 124 | country * COUNTRY 125 | runwayLength AIRPORT HEMA#DOUBLE 126 | northeast * * 127 | placeOfBirth * * 128 | state * POPULATEDPLACE 129 | absMagnitude * * 130 | headquarter ORGANISATION POPULATEDPLACE 131 | diameter * HEMA#DOUBLE 132 | ordo * * 133 | jurisdiction * * 134 | dean EDUCATIONALINSTITUTION PERSON 135 | governor * PERSON 136 | activeYearsEndDate * HEMA#DATE 137 | minimumTemperature PLANET HEMA#DOUBLE 138 | operator * * 139 | populationTotal * HEMA#NONNEGATIVEINTEGER 140 | r1Surface * * 141 | academicStaff * * 142 | owner * AGENT 143 | primeMinister * PERSON 144 | placeOfDeath * * 145 | doctoralStudents * * 146 | impactFactor ACADEMICJOURNAL HEMA#DOUBLE 147 | r3Surface * * 148 | ingredientName FOOD HEMA#STRING 149 | familia * * 150 | profession PERSON * 151 | region * PLACE 152 | r1Number * * 153 | commander * PERSON 154 | layout AUTOMOBILE * 155 | employer PERSON ORGANISATION 156 | district PLACE POPULATEDPLACE 157 | office * HEMA#STRING 158 | range MEANOFTRANSPORTATION_,_INSTRUMENT HEMA#POSITIVEINTEGER 159 | r4LengthF * * 160 | protein FOOD HEMA#DOUBLE 161 | deathDate PERSON HEMA#DATE 162 | developer * AGENT 163 | faculty * * 164 | crew2Up * * 165 | part * PLACE 166 | stateOfOrigin PERSON COUNTRY 167 | firstAired * * 168 | termStart * * 169 | Planet/surfaceArea PLANET SQUAREKILOMETRE 170 | shipClass * * 171 | currentclub * * 172 | firstPublicationYear PERIODICALLITERATURE HEMA#GYEAR 173 | r3LengthF * * 174 | predecessor * * 175 | stages * * 176 | students * * 177 | shipDisplacement SHIP HEMA#DOUBLE 178 | MeanOfTransportation/diameter MEANOFTRANSPORTATION METRE 179 | subdivisionName ISLAND PLACE 180 | finalFlight ROCKET HEMA#DATE 181 | founder * PERSON 182 | maidenVoyage SHIP HEMA#DATE 183 | genus SPECIES * 184 | arrondissement POPULATEDPLACE POPULATEDPLACE 185 | affiliations * * 186 | largestCity POPULATEDPLACE POPULATEDPLACE 187 | location * PLACE 188 | season AGENT * 189 | parentCompany * COMPANY 190 | mascot * HEMA#STRING 191 | previousWork WORK WORK 192 | width * HEMA#DOUBLE 193 | rotationPeriod PLANET HEMA#DOUBLE 194 | r2Surface * * 195 | areaLand PLACE HEMA#DOUBLE 196 | children * * 197 | nrhpReferenceNumber HISTORICPLACE HEMA#STRING 198 | eissn * * 199 | engine AUTOMOBILE AUTOMOBILEENGINE 200 | fossil SPECIES SPECIES 201 | west * * 202 | followedBy * * 203 | significantBuildings * * 204 | mass * HEMA#DOUBLE 205 | congress * * 206 | team * SPORTSTEAM 207 | network BROADCASTER BROADCASTER 208 | alias * -RDF-SYNTAX-NS#LANGSTRING 209 | function * * 210 | architecture * * 211 | Planet/temperature PLANET KELVIN 212 | hometown AGENT SETTLEMENT 213 | capacity * HEMA#NONNEGATIVEINTEGER 214 | numberOfPages WRITTENWORK HEMA#POSITIVEINTEGER 215 | shipOrdered * * 216 | headquarters * * 217 | associatedBand * BAND 218 | buildingEndDate * HEMA#STRING 219 | servingTemperature FOOD HEMA#STRING 220 | youthclubs * * 221 | class MEANOFTRANSPORTATION * 222 | singleTemperature * * 223 | series * * 224 | floorCount BUILDING HEMA#POSITIVEINTEGER 225 | dateOfRet * * 226 | chrtitle * * 227 | frequency * HEMA#DOUBLE 228 | discoverer * PERSON 229 | surfaceArea PLANET HEMA#DOUBLE 230 | partsType * * 231 | capital POPULATEDPLACE CITY 232 | occupation * PERSONFUNCTION 233 | neighboringMunicipalities * * 234 | utcOffset PLACE HEMA#STRING 235 | currentTenants * * 236 | startDate EVENT HEMA#DATE 237 | musicFusionGenre MUSICGENRE MUSICGENRE 238 | bandMember BAND PERSON 239 | locationTown * * 240 | formerTeam ATHLETE SPORTSTEAM 241 | athletics UNIVERSITY * 242 | shipLaunch SHIP HEMA#DATE 243 | militaryBranch MILITARYPERSON MILITARYUNIT 244 | mission * SPACEMISSION 245 | birthName PERSON -RDF-SYNTAX-NS#LANGSTRING 246 | facultySize EDUCATIONALINSTITUTION HEMA#NONNEGATIVEINTEGER 247 | nationality PERSON COUNTRY 248 | comparable ROCKET ROCKET 249 | countySeat POPULATEDPLACE * 250 | p * * 251 | architect ARCHITECTURALSTRUCTURE ARCHITECT 252 | formerName * -RDF-SYNTAX-NS#LANGSTRING 253 | openingDate * HEMA#DATE 254 | coden WRITTENWORK HEMA#STRING 255 | managerClub ATHLETE SPORTSTEAM 256 | representative PLACE HEMA#NONNEGATIVEINTEGER 257 | rank * HEMA#STRING 258 | keyPerson * PERSON 259 | senators * * 260 | genre * GENRE 261 | primeminister * * 262 | draftTeam ATHLETE SPORTSTEAM 263 | distributor * ORGANISATION 264 | campus UNIVERSITY * 265 | absoluteMagnitude CELESTIALBODY HEMA#DOUBLE 266 | manager SPORTSTEAM PERSON 267 | orbitalPeriod PLANET HEMA#DOUBLE 268 | floorArea BUILDING HEMA#DOUBLE 269 | director FILM PERSON 270 | builder * * 271 | issn PERIODICALLITERATURE HEMA#STRING 272 | affiliation ORGANISATION ORGANISATION 273 | gemstone * * 274 | transmission AUTOMOBILE HEMA#STRING 275 | foundationPlace ORGANISATION CITY 276 | r5Number * * 277 | sites * * 278 | discipline AGENT * 279 | elevation PLACE HEMA#DOUBLE 280 | isbn WRITTENWORK HEMA#STRING 281 | lid * * 282 | keyPeople * * 283 | Planet/density PLANET KILOGRAMPERCUBICMETRE 284 | musicSubgenre MUSICGENRE MUSICGENRE 285 | runwaySurface AIRPORT HEMA#STRING 286 | history * * 287 | alterEgo * * 288 | material ISLAND HEMA#STRING 289 | mainIngredient * * 290 | averageSpeed * HEMA#DOUBLE 291 | topSpeed * HEMA#DOUBLE 292 | undergrad * * 293 | discovered CELESTIALBODY HEMA#DATE 294 | birthDate PERSON HEMA#DATE 295 | coach * PERSON 296 | firstAppearance FICTIONALCHARACTER HEMA#STRING 297 | address PLACE -RDF-SYNTAX-NS#LANGSTRING 298 | Planet/averageSpeed PLANET KILOMETREPERSECOND 299 | child PERSON PERSON 300 | almaMater PERSON EDUCATIONALINSTITUTION 301 | managerclubs * * 302 | draftRound GRIDIRONFOOTBALLPLAYER HEMA#STRING 303 | stylisticOrigin MUSICGENRE MUSICGENRE 304 | shipOwner * * 305 | iataLocationIdentifier INFRASTRUCTURE HEMA#STRING 306 | academicDiscipline ACADEMICJOURNAL * 307 | stylisticOrigins * * 308 | aka * * 309 | aircraftHelicopter MILITARYUNIT MEANOFTRANSPORTATION 310 | totalLaunches * HEMA#NONNEGATIVEINTEGER 311 | alternativeNames * * 312 | debutTeam ATHLETE SPORTSTEAM 313 | tenant ARCHITECTURALSTRUCTURE ORGANISATION 314 | elevationF * * 315 | starring WORK ACTOR 316 | elevationM * * 317 | areaCode PLACE HEMA#STRING 318 | hubs * * 319 | largestcity * * 320 | author WORK PERSON 321 | demonym * -RDF-SYNTAX-NS#LANGSTRING 322 | title * -RDF-SYNTAX-NS#LANGSTRING 323 | aircraftTransport MILITARYUNIT MEANOFTRANSPORTATION 324 | league * SPORTSLEAGUE 325 | selection ASTRONAUT * 326 | related * * 327 | shipInService * * 328 | rockets * * 329 | added HISTORICPLACE HEMA#DATE 330 | maidenFlight ROCKET HEMA#DATE 331 | deathPlace PERSON PLACE 332 | origin * POPULATEDPLACE 333 | servingSize FOOD HEMA#DOUBLE 334 | city * CITY 335 | battle * MILITARYCONFLICT 336 | powertype * * 337 | shipBuilder * * 338 | battles * * 339 | locationCountry * COUNTRY 340 | parent PERSON PERSON 341 | awards * * 342 | branch * * 343 | chancellor * PERSON 344 | ethnicGroups * * 345 | shipChristened * * 346 | shipCompleted * * 347 | dateOfBirth * * 348 | r5Surface * * 349 | garrison MILITARYUNIT POPULATEDPLACE 350 | creators * * 351 | motto * HEMA#STRING 352 | ingredient FOOD * 353 | currency * CURRENCY 354 | cylindercount * * 355 | faa * * 356 | website * * 357 | product ORGANISATION * 358 | canton SETTLEMENT SETTLEMENT 359 | leaderTitle POPULATEDPLACE -RDF-SYNTAX-NS#LANGSTRING 360 | label * * 361 | builddate * * 362 | numberOfPostgraduateStudents UNIVERSITY HEMA#NONNEGATIVEINTEGER 363 | r1LengthF * * 364 | lccn WRITTENWORK HEMA#STRING 365 | chief1Name * * 366 | relatedMeanOfTransportation MEANOFTRANSPORTATION MEANOFTRANSPORTATION 367 | serviceStartYear MILITARYPERSON HEMA#GYEAR 368 | chairman * PERSON 369 | cost * HEMA#DOUBLE 370 | nativeName * * 371 | language * LANGUAGE 372 | manufacturer * ORGANISATION 373 | latinName ANATOMICALSTRUCTURE HEMA#STRING 374 | deathYear PERSON HEMA#GYEAR 375 | tenants * * 376 | sport * SPORT 377 | mediaType WRITTENWORK * 378 | leaderParty POPULATEDPLACE * 379 | religion * * 380 | regionServed ORGANISATION PLACE 381 | associatedMusicalArtist * MUSICALARTIST 382 | voice * PERSON 383 | president ORGANISATION PERSON 384 | deputy * PERSON 385 | higher * * 386 | bedCount HOSPITAL HEMA#NONNEGATIVEINTEGER 387 | rector EDUCATIONALINSTITUTION PERSON 388 | outlook * * 389 | colour * COLOUR 390 | southeast * * 391 | subsidiary COMPANY COMPANY 392 | fullname * * 393 | residence PERSON PLACE 394 | award * AWARD 395 | successor * * 396 | settlementType * * 397 | owningCompany * COMPANY 398 | powerType MEANOFTRANSPORTATION * 399 | doctoralAdvisor SCIENTIST PERSON 400 | hasVariant * * 401 | countryOrigin ROCKET COUNTRY 402 | foundedBy * AGENT 403 | editor * AGENT 404 | significantBuilding ARCHITECT BUILDING 405 | hubAirport AIRLINE AIRPORT 406 | areaWater PLACE HEMA#DOUBLE 407 | southwest * * 408 | postgrad * * 409 | periapsis CELESTIALBODY HEMA#DOUBLE 410 | Planet/orbitalPeriod PLANET DAY 411 | activeYearsStartYear * HEMA#GYEAR 412 | unrankedDivisio * * 413 | built * * 414 | locationCity ORGANISATION CITY 415 | crewMembers * * 416 | spouse PERSON PERSON 417 | bodyStyle AUTOMOBILE * 418 | legislature * * 419 | patronSaint SETTLEMENT PERSON 420 | bird PLACE SPECIES 421 | birthYear PERSON HEMA#GYEAR 422 | numberOfUndergraduateStudents EDUCATIONALINSTITUTION HEMA#NONNEGATIVEINTEGER 423 | literaryGenre WRITTENWORK * 424 | birthPlace PERSON PLACE 425 | apoapsis CELESTIALBODY HEMA#DOUBLE 426 | avgSpeed * * 427 | isPartOfMilitaryConflict MILITARYCONFLICT MILITARYCONFLICT 428 | Person/weight PERSON KILOGRAM 429 | temperature * HEMA#DOUBLE 430 | modelYears * * 431 | leaderName POPULATEDPLACE PERSON 432 | populationDensity POPULATEDPLACE HEMA#DOUBLE 433 | impact * * 434 | launchSite SPACEMISSION BUILDING 435 | Planet/meanTemperature PLANET KELVIN 436 | education PERSON * 437 | militaryRank MILITARYPERSON * 438 | monarch * PERSON 439 | fullName * * 440 | status * HEMA#STRING 441 | oclc WRITTENWORK HEMA#STRING 442 | recordLabel * RECORDLABEL 443 | unrankedOrdo * * 444 | governmentType * GOVERNMENTTYPE 445 | chairperson ORGANISATION PERSON 446 | vicePresident * PERSON 447 | mayor * MAYOR 448 | influencedBy * * 449 | nickname * * 450 | -------------------------------------------------------------------------------- /metadata/properties.list: -------------------------------------------------------------------------------- 1 | ribbon 2 | nearestCity 3 | enginetype 4 | year 5 | family 6 | established 7 | derivative 8 | crew1Up 9 | Planet/minimumTemperature 10 | Planet/periapsis 11 | derivatives 12 | Planet/maximumTemperature 13 | timeInSpace 14 | subsid 15 | epoch 16 | notableWork 17 | unit 18 | division 19 | activeYearsStartDate 20 | modelStartYear 21 | party 22 | variations 23 | dateOfDeath 24 | course 25 | targetAirport 26 | instrument 27 | MeanOfTransportation/length 28 | ground 29 | served 30 | years 31 | numberOfStudents 32 | creatorOfDish 33 | yearOfConstruction 34 | subsequentWork 35 | languages 36 | associatedActs 37 | Person/height 38 | beds 39 | champions 40 | shipMaidenVoyage 41 | lastAired 42 | runwayDesignation 43 | nativenameA 44 | position 45 | publisher 46 | productionEndYear 47 | spokenIn 48 | deathCause 49 | length 50 | site 51 | northwest 52 | unrankedClassis 53 | architecturalStyle 54 | shipDraft 55 | foundation 56 | similarDish 57 | anthem 58 | orderDate 59 | buildingStartDate 60 | minTemp 61 | inaugurationDate 62 | draftPick 63 | governingBody 64 | north 65 | order 66 | rocketStages 67 | shipBeam 68 | alternateName 69 | assembly 70 | designCompany 71 | wheelbase 72 | significantProject 73 | aircraftFighter 74 | municipality 75 | areaTotal 76 | height 77 | cityServed 78 | ethnicGroup 79 | abbreviation 80 | generalManager 81 | shipOperator 82 | Building/floorArea 83 | Planet/apoapsis 84 | category 85 | density 86 | buildingType 87 | background 88 | colours 89 | numberOfRooms 90 | christeningDate 91 | creator 92 | clubs 93 | sportGoverningBody 94 | icaoLocationIdentifier 95 | leader 96 | river 97 | Planet/mass 98 | fat 99 | r4Surface 100 | doctoralStudent 101 | officialSchoolColour 102 | nationalteam 103 | neighboringMunicipality 104 | first 105 | r1LengthM 106 | aircraftAttack 107 | escapeVelocity 108 | completionDate 109 | designer 110 | fate 111 | free 112 | dedicatedTo 113 | postalCode 114 | foundingDate 115 | precededBy 116 | union 117 | isPartOf 118 | distributingCompany 119 | activeYearsEndYear 120 | open 121 | carbohydrate 122 | officialLanguage 123 | owningOrganisation 124 | country 125 | runwayLength 126 | northeast 127 | placeOfBirth 128 | state 129 | absMagnitude 130 | headquarter 131 | diameter 132 | ordo 133 | jurisdiction 134 | dean 135 | governor 136 | activeYearsEndDate 137 | minimumTemperature 138 | operator 139 | populationTotal 140 | r1Surface 141 | academicStaff 142 | owner 143 | primeMinister 144 | placeOfDeath 145 | doctoralStudents 146 | impactFactor 147 | r3Surface 148 | ingredientName 149 | familia 150 | profession 151 | region 152 | r1Number 153 | commander 154 | layout 155 | employer 156 | district 157 | office 158 | range 159 | r4LengthF 160 | protein 161 | deathDate 162 | developer 163 | faculty 164 | crew2Up 165 | part 166 | stateOfOrigin 167 | firstAired 168 | termStart 169 | Planet/surfaceArea 170 | shipClass 171 | currentclub 172 | firstPublicationYear 173 | r3LengthF 174 | predecessor 175 | stages 176 | students 177 | shipDisplacement 178 | MeanOfTransportation/diameter 179 | subdivisionName 180 | finalFlight 181 | founder 182 | maidenVoyage 183 | genus 184 | arrondissement 185 | affiliations 186 | largestCity 187 | location 188 | season 189 | parentCompany 190 | mascot 191 | previousWork 192 | width 193 | rotationPeriod 194 | r2Surface 195 | areaLand 196 | children 197 | nrhpReferenceNumber 198 | eissn 199 | engine 200 | fossil 201 | west 202 | followedBy 203 | significantBuildings 204 | mass 205 | congress 206 | team 207 | network 208 | alias 209 | function 210 | architecture 211 | Planet/temperature 212 | hometown 213 | capacity 214 | numberOfPages 215 | shipOrdered 216 | headquarters 217 | associatedBand 218 | buildingEndDate 219 | servingTemperature 220 | youthclubs 221 | class 222 | singleTemperature 223 | series 224 | floorCount 225 | dateOfRet 226 | chrtitle 227 | frequency 228 | discoverer 229 | surfaceArea 230 | partsType 231 | capital 232 | occupation 233 | neighboringMunicipalities 234 | utcOffset 235 | currentTenants 236 | startDate 237 | musicFusionGenre 238 | bandMember 239 | locationTown 240 | formerTeam 241 | athletics 242 | shipLaunch 243 | militaryBranch 244 | mission 245 | birthName 246 | facultySize 247 | nationality 248 | comparable 249 | countySeat 250 | p 251 | architect 252 | formerName 253 | openingDate 254 | coden 255 | managerClub 256 | representative 257 | rank 258 | keyPerson 259 | senators 260 | genre 261 | primeminister 262 | draftTeam 263 | distributor 264 | campus 265 | absoluteMagnitude 266 | manager 267 | orbitalPeriod 268 | floorArea 269 | director 270 | builder 271 | issn 272 | affiliation 273 | gemstone 274 | transmission 275 | foundationPlace 276 | r5Number 277 | sites 278 | discipline 279 | elevation 280 | isbn 281 | lid 282 | keyPeople 283 | Planet/density 284 | musicSubgenre 285 | runwaySurface 286 | history 287 | alterEgo 288 | material 289 | mainIngredient 290 | averageSpeed 291 | topSpeed 292 | undergrad 293 | discovered 294 | birthDate 295 | coach 296 | firstAppearance 297 | address 298 | Planet/averageSpeed 299 | child 300 | almaMater 301 | managerclubs 302 | draftRound 303 | stylisticOrigin 304 | shipOwner 305 | iataLocationIdentifier 306 | academicDiscipline 307 | stylisticOrigins 308 | aka 309 | aircraftHelicopter 310 | totalLaunches 311 | alternativeNames 312 | debutTeam 313 | tenant 314 | elevationF 315 | starring 316 | elevationM 317 | areaCode 318 | hubs 319 | largestcity 320 | author 321 | demonym 322 | title 323 | aircraftTransport 324 | league 325 | selection 326 | related 327 | shipInService 328 | rockets 329 | added 330 | maidenFlight 331 | deathPlace 332 | origin 333 | servingSize 334 | city 335 | battle 336 | powertype 337 | shipBuilder 338 | battles 339 | locationCountry 340 | parent 341 | awards 342 | branch 343 | chancellor 344 | ethnicGroups 345 | shipChristened 346 | shipCompleted 347 | dateOfBirth 348 | r5Surface 349 | garrison 350 | creators 351 | motto 352 | ingredient 353 | currency 354 | cylindercount 355 | faa 356 | website 357 | product 358 | canton 359 | leaderTitle 360 | label 361 | builddate 362 | numberOfPostgraduateStudents 363 | r1LengthF 364 | lccn 365 | chief1Name 366 | relatedMeanOfTransportation 367 | serviceStartYear 368 | chairman 369 | cost 370 | nativeName 371 | language 372 | manufacturer 373 | latinName 374 | deathYear 375 | tenants 376 | sport 377 | mediaType 378 | leaderParty 379 | religion 380 | regionServed 381 | associatedMusicalArtist 382 | voice 383 | president 384 | deputy 385 | higher 386 | bedCount 387 | rector 388 | outlook 389 | colour 390 | southeast 391 | subsidiary 392 | fullname 393 | residence 394 | award 395 | successor 396 | settlementType 397 | owningCompany 398 | powerType 399 | doctoralAdvisor 400 | hasVariant 401 | countryOrigin 402 | foundedBy 403 | editor 404 | significantBuilding 405 | hubAirport 406 | areaWater 407 | southwest 408 | postgrad 409 | periapsis 410 | Planet/orbitalPeriod 411 | activeYearsStartYear 412 | unrankedDivisio 413 | built 414 | locationCity 415 | crewMembers 416 | spouse 417 | bodyStyle 418 | legislature 419 | patronSaint 420 | bird 421 | birthYear 422 | numberOfUndergraduateStudents 423 | literaryGenre 424 | birthPlace 425 | apoapsis 426 | avgSpeed 427 | isPartOfMilitaryConflict 428 | Person/weight 429 | temperature 430 | modelYears 431 | leaderName 432 | populationDensity 433 | impact 434 | launchSite 435 | Planet/meanTemperature 436 | education 437 | militaryRank 438 | monarch 439 | fullName 440 | status 441 | oclc 442 | recordLabel 443 | unrankedOrdo 444 | governmentType 445 | chairperson 446 | vicePresident 447 | mayor 448 | influencedBy 449 | nickname -------------------------------------------------------------------------------- /multi-bleu.perl: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env perl 2 | # 3 | # This file is part of moses. Its use is licensed under the GNU Lesser General 4 | # Public License version 2.1 or, at your option, any later version. 5 | 6 | # $Id$ 7 | use warnings; 8 | use strict; 9 | 10 | my $lowercase = 0; 11 | if ($ARGV[0] eq "-lc") { 12 | $lowercase = 1; 13 | shift; 14 | } 15 | 16 | my $stem = $ARGV[0]; 17 | if (!defined $stem) { 18 | print STDERR "usage: multi-bleu.pl [-lc] reference < hypothesis\n"; 19 | print STDERR "Reads the references from reference or reference0, reference1, ...\n"; 20 | exit(1); 21 | } 22 | 23 | $stem .= ".ref" if !-e $stem && !-e $stem."0" && -e $stem.".ref0"; 24 | 25 | my @REF; 26 | my $ref=0; 27 | while(-e "$stem$ref") { 28 | &add_to_ref("$stem$ref",\@REF); 29 | $ref++; 30 | } 31 | &add_to_ref($stem,\@REF) if -e $stem; 32 | die("ERROR: could not find reference file $stem") unless scalar @REF; 33 | 34 | # add additional references explicitly specified on the command line 35 | shift; 36 | foreach my $stem (@ARGV) { 37 | &add_to_ref($stem,\@REF) if -e $stem; 38 | } 39 | 40 | 41 | 42 | sub add_to_ref { 43 | my ($file,$REF) = @_; 44 | my $s=0; 45 | if ($file =~ /.gz$/) { 46 | open(REF,"gzip -dc $file|") or die "Can't read $file"; 47 | } else { 48 | open(REF,$file) or die "Can't read $file"; 49 | } 50 | while() { 51 | chop; 52 | push @{$$REF[$s++]}, $_; 53 | } 54 | close(REF); 55 | } 56 | 57 | my(@CORRECT,@TOTAL,$length_translation,$length_reference); 58 | my $s=0; 59 | while() { 60 | chop; 61 | $_ = lc if $lowercase; 62 | my @WORD = split; 63 | my %REF_NGRAM = (); 64 | my $length_translation_this_sentence = scalar(@WORD); 65 | my ($closest_diff,$closest_length) = (9999,9999); 66 | foreach my $reference (@{$REF[$s]}) { 67 | # print "$s $_ <=> $reference\n"; 68 | $reference = lc($reference) if $lowercase; 69 | my @WORD = split(' ',$reference); 70 | my $length = scalar(@WORD); 71 | my $diff = abs($length_translation_this_sentence-$length); 72 | if ($diff < $closest_diff) { 73 | $closest_diff = $diff; 74 | $closest_length = $length; 75 | # print STDERR "$s: closest diff ".abs($length_translation_this_sentence-$length)." = abs($length_translation_this_sentence-$length), setting len: $closest_length\n"; 76 | } elsif ($diff == $closest_diff) { 77 | $closest_length = $length if $length < $closest_length; 78 | # from two references with the same closeness to me 79 | # take the *shorter* into account, not the "first" one. 80 | } 81 | for(my $n=1;$n<=4;$n++) { 82 | my %REF_NGRAM_N = (); 83 | for(my $start=0;$start<=$#WORD-($n-1);$start++) { 84 | my $ngram = "$n"; 85 | for(my $w=0;$w<$n;$w++) { 86 | $ngram .= " ".$WORD[$start+$w]; 87 | } 88 | $REF_NGRAM_N{$ngram}++; 89 | } 90 | foreach my $ngram (keys %REF_NGRAM_N) { 91 | if (!defined($REF_NGRAM{$ngram}) || 92 | $REF_NGRAM{$ngram} < $REF_NGRAM_N{$ngram}) { 93 | $REF_NGRAM{$ngram} = $REF_NGRAM_N{$ngram}; 94 | # print "$i: REF_NGRAM{$ngram} = $REF_NGRAM{$ngram}
\n"; 95 | } 96 | } 97 | } 98 | } 99 | $length_translation += $length_translation_this_sentence; 100 | $length_reference += $closest_length; 101 | for(my $n=1;$n<=4;$n++) { 102 | my %T_NGRAM = (); 103 | for(my $start=0;$start<=$#WORD-($n-1);$start++) { 104 | my $ngram = "$n"; 105 | for(my $w=0;$w<$n;$w++) { 106 | $ngram .= " ".$WORD[$start+$w]; 107 | } 108 | $T_NGRAM{$ngram}++; 109 | } 110 | foreach my $ngram (keys %T_NGRAM) { 111 | $ngram =~ /^(\d+) /; 112 | my $n = $1; 113 | # my $corr = 0; 114 | # print "$i e $ngram $T_NGRAM{$ngram}
\n"; 115 | $TOTAL[$n] += $T_NGRAM{$ngram}; 116 | if (defined($REF_NGRAM{$ngram})) { 117 | if ($REF_NGRAM{$ngram} >= $T_NGRAM{$ngram}) { 118 | $CORRECT[$n] += $T_NGRAM{$ngram}; 119 | # $corr = $T_NGRAM{$ngram}; 120 | # print "$i e correct1 $T_NGRAM{$ngram}
\n"; 121 | } 122 | else { 123 | $CORRECT[$n] += $REF_NGRAM{$ngram}; 124 | # $corr = $REF_NGRAM{$ngram}; 125 | # print "$i e correct2 $REF_NGRAM{$ngram}
\n"; 126 | } 127 | } 128 | # $REF_NGRAM{$ngram} = 0 if !defined $REF_NGRAM{$ngram}; 129 | # print STDERR "$ngram: {$s, $REF_NGRAM{$ngram}, $T_NGRAM{$ngram}, $corr}\n" 130 | } 131 | } 132 | $s++; 133 | } 134 | my $brevity_penalty = 1; 135 | my $bleu = 0; 136 | 137 | my @bleu=(); 138 | 139 | for(my $n=1;$n<=4;$n++) { 140 | if (defined ($TOTAL[$n])){ 141 | $bleu[$n]=($TOTAL[$n])?$CORRECT[$n]/$TOTAL[$n]:0; 142 | # print STDERR "CORRECT[$n]:$CORRECT[$n] TOTAL[$n]:$TOTAL[$n]\n"; 143 | }else{ 144 | $bleu[$n]=0; 145 | } 146 | } 147 | 148 | if ($length_reference==0){ 149 | printf "BLEU = 0, 0/0/0/0 (BP=0, ratio=0, hyp_len=0, ref_len=0)\n"; 150 | exit(1); 151 | } 152 | 153 | if ($length_translation<$length_reference) { 154 | $brevity_penalty = exp(1-$length_reference/$length_translation); 155 | } 156 | $bleu = $brevity_penalty * exp((my_log( $bleu[1] ) + 157 | my_log( $bleu[2] ) + 158 | my_log( $bleu[3] ) + 159 | my_log( $bleu[4] ) ) / 4) ; 160 | printf "BLEU = %.2f, %.1f/%.1f/%.1f/%.1f (BP=%.3f, ratio=%.3f, hyp_len=%d, ref_len=%d)\n", 161 | 100*$bleu, 162 | 100*$bleu[1], 163 | 100*$bleu[2], 164 | 100*$bleu[3], 165 | 100*$bleu[4], 166 | $brevity_penalty, 167 | $length_translation / $length_reference, 168 | $length_translation, 169 | $length_reference; 170 | 171 | sub my_log { 172 | return -9999999999 unless $_[0]; 173 | return log($_[0]); 174 | } 175 | -------------------------------------------------------------------------------- /relex_predictions.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program that takes the text generated by a neural network and a 3 | relexicalization table to produce text for evaluation. 4 | """ 5 | 6 | import argparse 7 | 8 | def generate(): 9 | """ 10 | Read options from user input, and final textual output. 11 | """ 12 | 13 | DISCR = 'Perfrom relexicalization on the predictions of DNN.' 14 | parser = argparse.ArgumentParser(description=DISCR) 15 | parser.add_argument('-pred', type=str, help='Path to prediction file.', 16 | required=True) 17 | 18 | parser.add_argument('-relex', help='File to relexicalization table.', 19 | required=True) 20 | 21 | parser.add_argument('-output', type=str, help='Path to output file for final predictions.', 22 | required=True) 23 | 24 | args = parser.parse_args() 25 | 26 | # read from preditions file 27 | with open(args.pred, encoding="utf8") as predF: 28 | predictions = predF.readlines() 29 | 30 | # read relex table 31 | relex_table = [] 32 | 33 | with open(args.relex, encoding="utf8") as relexF: 34 | lines = relexF.readlines() 35 | 36 | for line in lines: 37 | relex_entities = line.split('\t') 38 | 39 | instance_relex = {} 40 | 41 | for (k, e) in enumerate(relex_entities): 42 | instance_relex['ENTITY_' + str(k+1)] = e.replace('"', '').replace('\n', ' ') 43 | 44 | relex_table.append(instance_relex) 45 | 46 | 47 | # sanity check 48 | assert len(predictions) == len(relex_table), \ 49 | "Length of prediction file and relex table are not equal." 50 | 51 | 52 | #print(relex_table) 53 | final_predictions = [] 54 | 55 | for (pred, relex) in zip(predictions, relex_table): 56 | predX = pred 57 | for (id, lex) in relex.items(): 58 | predX = predX.replace(id, lex) 59 | 60 | final_predictions.append(predX) 61 | 62 | assert len(predictions) == len(final_predictions), \ 63 | "Length of prediction file and relex table are not equal." 64 | 65 | with open(args.output, 'a+', encoding="utf8") as opFile: 66 | opFile.write(''.join(final_predictions)) 67 | 68 | def main(): 69 | generate() 70 | 71 | if __name__ == '__main__': 72 | main() 73 | -------------------------------------------------------------------------------- /support/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/badrex/rdf2text/bdc72db893d76972af196123b71ef0c6b2d3e552/support/__init__.py -------------------------------------------------------------------------------- /support/generate_eval_metadata.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to process RDF data and generate metadata (e.g. size, category) 3 | for evaluation and testing. 4 | """ 5 | 6 | from utils import rdf_utils 7 | import argparse 8 | from graph2text import * 9 | 10 | def generate(): 11 | """ 12 | Read options from user input, and generate eval dataset. 13 | """ 14 | 15 | DISCR = 'Generate dataset from XML files of RDF to Text Entries.' 16 | parser = argparse.ArgumentParser(description=DISCR) 17 | parser.add_argument('-path', type=str, help='Path to tgt data.', 18 | required=True) 19 | 20 | parser.add_argument('-seen', type=str, help='SEEN or UNSEEN.', 21 | required=True) 22 | 23 | parser.add_argument('-output', type=str, help='Path to output file.', 24 | required=True) 25 | 26 | args = parser.parse_args() 27 | 28 | instances, _, _ = rdf_utils.generate_instances(args.path, eval=True) 29 | 30 | counter = 1 31 | 32 | for (size, ins) in instances.items(): 33 | for i in ins: 34 | id = counter 35 | size = i.size 36 | cat = i.category 37 | 38 | mdata = [id, int(size), cat, args.seen] 39 | 40 | print(mdata) 41 | 42 | # write to a file 43 | with open(args.output, 'a+', encoding="utf8") as outputFile: 44 | outputFile.write('\t'.join(str(j) for j in mdata) + '\n') 45 | 46 | counter += 1 47 | 48 | 49 | def main(): 50 | generate() 51 | 52 | if __name__ == '__main__': 53 | main() 54 | -------------------------------------------------------------------------------- /support/generate_lists.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to process RDF data and generate lists of entities and properties. 3 | """ 4 | 5 | from utils import rdf_utils 6 | import argparse 7 | import os 8 | 9 | 10 | def generate(): 11 | """ 12 | Read options from user input, and generate dataset. 13 | """ 14 | 15 | DISCR = 'Generate lists of entities and properties from RDF data.' 16 | parser = argparse.ArgumentParser(description=DISCR) 17 | parser.add_argument('-path', type=str, help='Path to data.', required=True) 18 | 19 | parser.add_argument('-entity_list', type=str, help='Path to entities file.', 20 | required=True) 21 | parser.add_argument('-prop_list', type=str, help='Path to properties file.', 22 | required=True) 23 | 24 | args = parser.parse_args() 25 | 26 | subdirs = [f.path for f in os.scandir(args.path) if f.is_dir()] 27 | 28 | e_list = set() 29 | p_list = set() 30 | 31 | for data_dir in subdirs: 32 | _, entities, properties = rdf_utils.generate_instances(data_dir, True) 33 | e_list.update(entities) 34 | p_list.update(properties) 35 | 36 | print(data_dir, len(entities), len(properties)) 37 | print(data_dir, len(e_list), len(p_list)) 38 | 39 | print(len(e_list), len(p_list)) 40 | 41 | with open(args.entity_list, '+a') as eFile: 42 | eFile.write("\n".join(e_list)) 43 | 44 | with open(args.prop_list, '+a') as pFile: 45 | pFile.write("\n".join(p_list)) 46 | 47 | 48 | def main(): 49 | generate() 50 | 51 | if __name__ == '__main__': 52 | main() 53 | -------------------------------------------------------------------------------- /support/generate_nodelex.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to generate a list of entities which could not be delex. 3 | NOTE: Only for testing purposes. 4 | """ 5 | 6 | from utils import rdf_utils 7 | import argparse 8 | from graph2text import * 9 | 10 | def generate(): 11 | """ 12 | Read options from user input, and generate list. 13 | """ 14 | 15 | DISCR = 'Generate list of entities from XML files of RDF to Text Entries.' 16 | parser = argparse.ArgumentParser(description=DISCR) 17 | parser.add_argument('-path', type=str, help='Path to data.', required=True) 18 | parser.add_argument('-output', help='Path to output file.', required=True) 19 | 20 | args = parser.parse_args() 21 | 22 | instances, _, _ = rdf_utils.generate_instances(args.path) 23 | 24 | not_delex = set() 25 | 26 | counter = 0 27 | for (size, ins) in instances.items(): 28 | for i in ins: 29 | tripleset = (i.originaltripleset, i.modifiedtripleset) 30 | G = FactGraph(tripleset, i.Lexicalisation.lex) 31 | _, nodelex = G.delexicalize_text(advanced=True) 32 | 33 | not_delex.update(nodelex) 34 | counter += 1 35 | 36 | if counter % 10 == 0: 37 | print("Processed so far: ", counter) 38 | 39 | print(len(not_delex)) 40 | 41 | with open(args.output, 'w+', encoding="utf8") as opFile: 42 | opFile.write("\n".join(str(e) + ' § ' + str(s) for (e, s) in not_delex)) 43 | 44 | 45 | def main(): 46 | generate() 47 | 48 | if __name__ == '__main__': 49 | main() 50 | -------------------------------------------------------------------------------- /support/generate_onto_classes_system.py: -------------------------------------------------------------------------------- 1 | import json 2 | import xml.etree.ElementTree as ET 3 | 4 | threshold = 1000 5 | 6 | with open('metadata/ontology_ponderated_occurences.json', encoding="utf8") as f: 7 | ontology_ponderated_occurences = json.load(f) 8 | 9 | system = dict() 10 | genealogy = 10*[""] 11 | 12 | def walker(elem, level): 13 | valid_relative = genealogy[level//2-1] 14 | try: 15 | text = elem.text 16 | if (text[0].isalpha()) & (text != "edit"): 17 | if text.upper() in ontology_ponderated_occurences: 18 | occurences = ontology_ponderated_occurences[text.upper()] 19 | else: 20 | occurences = 0 21 | if occurences > threshold or valid_relative == "": 22 | valid_relative = text 23 | system[text] = (level//2, valid_relative) 24 | genealogy[level//2] = valid_relative 25 | print('-'*level+'o '+text+' * '+valid_relative) 26 | except Exception: 27 | pass 28 | 29 | for child in elem.getchildren(): 30 | walker(child, level+1) 31 | 32 | root = ET.parse('metadata/OntologyClasses.xml') 33 | walker(root.getroot(), 0) 34 | 35 | with open('metadata/onto_classes_system.json', 'w') as f: 36 | json.dump(system, f, ensure_ascii=False, indent=3, sort_keys=True) 37 | -------------------------------------------------------------------------------- /support/get_entity_type.py: -------------------------------------------------------------------------------- 1 | """ 2 | For each property in the dataset, get its domain and range. 3 | """ 4 | 5 | from collections import defaultdict 6 | from utils import sparql_utils 7 | import argparse 8 | 9 | def generate(): 10 | """ 11 | Read options from user input, and generate output. 12 | """ 13 | 14 | DISCR = 'Get semantic type for entities in the RDF data.' 15 | parser = argparse.ArgumentParser(description=DISCR) 16 | parser.add_argument('-output', type=str, help='Path to output file.', required=True) 17 | 18 | args = parser.parse_args() 19 | 20 | with open('metadata/entities.list', encoding="utf8") as f: 21 | rdf_entity_list = [e.strip() for e in f.readlines()] 22 | 23 | entity_type = [] 24 | 25 | for (i, e) in enumerate(rdf_entity_list): 26 | stype = sparql_utils.get_resource_type(e) 27 | 28 | entity_type.append((e, stype)) 29 | 30 | if i != 0 and i % 10 == 0: 31 | print(i, "entities processed so far ...") 32 | 33 | 34 | with open(args.output, '+w') as pFile: 35 | for (e, stype) in entity_type: 36 | pFile.write(' '.join((e, stype)) + '\n') 37 | 38 | def main(): 39 | generate() 40 | 41 | if __name__ == '__main__': 42 | main() 43 | -------------------------------------------------------------------------------- /support/get_property_schema.py: -------------------------------------------------------------------------------- 1 | """ 2 | For each property in the dataset, get its domain and range. 3 | """ 4 | 5 | from collections import defaultdict 6 | from utils import sparql_utils 7 | import argparse 8 | 9 | def generate(): 10 | """ 11 | Read options from user input, and generate dataset. 12 | """ 13 | 14 | DISCR = 'Generate schema for properties in the RDF data.' 15 | parser = argparse.ArgumentParser(description=DISCR) 16 | parser.add_argument('-output', type=str, help='Path to output file.', required=True) 17 | 18 | args = parser.parse_args() 19 | 20 | with open('metadata/properties.list', encoding="utf8") as f: 21 | rdf_prop_list = [prop.strip() for prop in f.readlines()] 22 | 23 | prop_schema = [] 24 | 25 | for (i, prop) in enumerate(rdf_prop_list): 26 | prop_domain = sparql_utils.get_property_domain(prop) 27 | prop_range = sparql_utils.get_property_range(prop) 28 | 29 | prop_schema.append((prop, prop_domain, prop_range)) 30 | 31 | if i != 0 and i % 10 == 0: 32 | print(i, "properties processed so far ...") 33 | 34 | 35 | with open(args.output, '+w') as pFile: 36 | for (p, d, r) in prop_schema: 37 | pFile.write(' '.join((p, d, r)) + '\n') 38 | 39 | def main(): 40 | generate() 41 | 42 | if __name__ == '__main__': 43 | main() 44 | -------------------------------------------------------------------------------- /support/link_metadata.py: -------------------------------------------------------------------------------- 1 | """ 2 | A program to link metadata (e.g. size, category) to lexicalised predictions 3 | for evaluation and testing. 4 | """ 5 | 6 | import argparse 7 | 8 | def link(): 9 | """ 10 | Read options from user input, and generate eval dataset. 11 | """ 12 | 13 | DISCR = 'Link metadata to predictions.' 14 | parser = argparse.ArgumentParser(description=DISCR) 15 | parser.add_argument('-pred', type=str, help='Path to preditions.', 16 | required=True) 17 | 18 | parser.add_argument('-meta', type=str, help='Path to metadata.', 19 | required=True) 20 | 21 | parser.add_argument('-output', type=str, help='Path to output file.', 22 | required=True) 23 | 24 | args = parser.parse_args() 25 | 26 | with open(args.pred, 'r', encoding="utf8") as predFile: 27 | predictions = predFile.readlines() 28 | 29 | with open(args.meta, 'r', encoding="utf8") as metaFile: 30 | metadata = metaFile.readlines() 31 | 32 | 33 | for (d, p) in zip(metadata, predictions): 34 | # write to a file 35 | print(d, p) 36 | with open(args.output, 'a+', encoding="utf8") as outputFile: 37 | d = [i.replace('\n', '') for i in d.split()] 38 | d.append(p.replace('\n', '')) 39 | outputFile.write('\t'.join([j for j in d]) + '\n') 40 | 41 | 42 | def main(): 43 | link() 44 | 45 | if __name__ == '__main__': 46 | main() 47 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/badrex/rdf2text/bdc72db893d76972af196123b71ef0c6b2d3e552/utils/__init__.py -------------------------------------------------------------------------------- /utils/rdf_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module contains some useful classes and functions to deal with RDF data and 3 | read and process XML files. 4 | """ 5 | 6 | from nltk.tokenize import word_tokenize 7 | import xml.etree.ElementTree as et 8 | from collections import defaultdict 9 | import argparse 10 | import os 11 | 12 | 13 | class Triple: 14 | 15 | def __init__(self, s, p, o): 16 | self.subject = s 17 | self.object = o 18 | self.property = p 19 | 20 | 21 | class Tripleset: 22 | 23 | def __init__(self): 24 | self.triples = [] 25 | 26 | def fill_tripleset(self, t): 27 | for xml_triple in t: 28 | s, p, o = xml_triple.text.split(' | ') 29 | 30 | # inistiate a triple 31 | triple = Triple(s, p, o) 32 | self.triples.append(triple) 33 | 34 | 35 | class Lexicalisation: 36 | 37 | def __init__(self, lex, comment, lid): 38 | self.lex = lex 39 | self.comment = comment 40 | self.id = lid 41 | 42 | 43 | class Entry: 44 | 45 | def __init__(self, category, size, eid): 46 | self.originaltripleset = [] 47 | self.modifiedtripleset = Tripleset() 48 | self.lexs = [] 49 | self.category = category 50 | self.size = size 51 | self.id = eid 52 | 53 | def fill_originaltriple(self, xml_t): 54 | otripleset = Tripleset() 55 | self.originaltripleset.append(otripleset) # multiple originaltriplesets for one entry 56 | otripleset.fill_tripleset(xml_t) 57 | 58 | def fill_modifiedtriple(self, xml_t): 59 | self.modifiedtripleset.fill_tripleset(xml_t) 60 | 61 | def create_lex(self, xml_lex): 62 | comment = xml_lex.attrib['comment'] 63 | lid = xml_lex.attrib['lid'] 64 | lex = Lexicalisation(xml_lex.text, comment, lid) 65 | self.lexs.append(lex) 66 | 67 | def count_lexs(self): 68 | return len(self.lexs) 69 | 70 | 71 | class RDFInstance(object): 72 | 73 | def __init__(self, category, size, otripleset, mtripleset, lex=None): 74 | """ 75 | Instantiate RDFInstance object. 76 | :param category: category of entry (dtype: string) 77 | :param size: number of triples in entry (dtype: int) 78 | :param otripleset: a list original tripleset (dtype: list (Tripleset)) 79 | :param mtripleset: a modified tripleset (dtype: Tripleset) 80 | :param sentence: a sentence that realises the triple set for training 81 | instances 82 | :dtype: Lexicalisation object (to get text, Lexicalisation.lex) 83 | :default: None (for eval and test instances). 84 | """ 85 | 86 | self.category = category 87 | self.size = size 88 | self.originaltripleset = otripleset 89 | self.modifiedtripleset = mtripleset 90 | 91 | if lex: 92 | self.Lexicalisation = lex 93 | 94 | self.entities = set() 95 | self.properties = set() 96 | 97 | self._populate_sets() 98 | 99 | 100 | def _populate_sets(self): 101 | """ 102 | populate the two sets; entities and properties. 103 | """ 104 | for tset in self.originaltripleset: 105 | for triple in tset.triples: 106 | s = triple.subject 107 | p = triple.property 108 | o = triple.object 109 | 110 | self.entities.update((s, o)) 111 | self.properties.add(p) 112 | 113 | 114 | def parseXML(xml_file): 115 | """ 116 | Parse an xml file, return a list of RDF-Text instances (list(Entry)). 117 | :param category: xml_file string (dtype: string) 118 | """ 119 | 120 | entries = [] 121 | 122 | tree = et.parse(xml_file) 123 | root = tree.getroot() 124 | 125 | for xml_entry in root.iter('entry'): 126 | # to ignore triples with no lexicalisations 127 | # get a list of tags in the entry 128 | tags = [c.tag for c in xml_entry] 129 | 130 | if "lex" not in tags: 131 | # skip this entry, move to next entry in the loop 132 | continue 133 | 134 | # get attributes 135 | entry_id = xml_entry.attrib['eid'] 136 | category = xml_entry.attrib['category'] 137 | size = xml_entry.attrib['size'] 138 | 139 | entry = Entry(category, size, entry_id) 140 | 141 | for element in xml_entry: 142 | if element.tag == 'originaltripleset': 143 | entry.fill_originaltriple(element) 144 | elif element.tag == 'modifiedtripleset': 145 | entry.fill_modifiedtriple(element) 146 | elif element.tag == 'lex': 147 | entry.create_lex(element) 148 | 149 | entries.append(entry) 150 | 151 | return entries 152 | 153 | 154 | def generate_instances(dir, extended=False, eval=False): 155 | """ 156 | Traverse through a path, visit all subdirectories, and return a dict of 157 | entries accessible by size: size --> list of entries 158 | :param dir: path to data files (dtype: string) 159 | :param extended: should it generate entities and properties? 160 | (dtype: bool, default: False) 161 | """ 162 | 163 | subfolders = [f.path for f in os.scandir(dir) if f.is_dir() ] 164 | 165 | instances = defaultdict(list) 166 | 167 | global_entities = set() 168 | global_properties = set() 169 | 170 | # loop through each dir 171 | for d in sorted(subfolders): 172 | xml_files = [f for f in os.listdir(d) if f.endswith('.xml')] 173 | 174 | # loop through each file in the directory 175 | for f in xml_files: 176 | # create new XMLParser object 177 | entries = parseXML(d + '/' + f) 178 | 179 | if eval: 180 | for entry in entries: 181 | rdfInstance = RDFInstance(entry.category, 182 | entry.size, 183 | entry.originaltripleset, 184 | entry.modifiedtripleset, 185 | entry.lexs) 186 | 187 | # append to list of instances 188 | instances[entry.size].append(rdfInstance) 189 | 190 | if extended: 191 | global_entities.update(rdfInstance.entities) 192 | global_properties.update(rdfInstance.properties) 193 | 194 | else: 195 | for entry in entries: 196 | for lex in entry.lexs: 197 | rdfInstance = RDFInstance(entry.category, 198 | entry.size, 199 | entry.originaltripleset, 200 | entry.modifiedtripleset, 201 | lex) 202 | 203 | # append to list of instances 204 | instances[entry.size].append(rdfInstance) 205 | 206 | if extended: 207 | global_entities.update(rdfInstance.entities) 208 | global_properties.update(rdfInstance.properties) 209 | 210 | return instances, global_entities, global_properties 211 | -------------------------------------------------------------------------------- /utils/sparql_utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | from SPARQLWrapper import SPARQLWrapper, JSON 3 | 4 | # dictionary: ontology class -> [precision score, closest valid parent class] 5 | # (an ontology class is valid if its frequency is higher than a defined threshold) 6 | with open('metadata/onto_classes_system.json', encoding="utf8") as f: 7 | onto_classes_system = json.load(f) 8 | 9 | def dbpedia_query(query_string, resource = False): 10 | sparql = SPARQLWrapper("http://dbpedia.org/sparql") 11 | sparql.setQuery(query_string) 12 | sparql.setReturnFormat(JSON) 13 | 14 | # query dbpedia about a resource/entity (subject or object of a triple) 15 | if resource: 16 | try: 17 | results = sparql.query().convert() 18 | 19 | # we only consider the dbpedia:ontology results 20 | ontology_url = "http://dbpedia.org/ontology/" 21 | onto_classes = [x["x"]["value"][28:] 22 | for x in results["results"]["bindings"] 23 | if x["x"]["value"].startswith(ontology_url)] 24 | 25 | bestPrecision = 0 26 | mostPreciseType = "THING" 27 | 28 | for type in onto_classes: 29 | if type in onto_classes_system: 30 | if onto_classes_system[type][0] > bestPrecision: 31 | mostPreciseType = type 32 | bestPrecision = onto_classes_system[type][0] 33 | 34 | # the ontology class returned is the closest valid parent 35 | # of the most precise class 36 | return onto_classes_system[mostPreciseType][1].upper() 37 | 38 | except Exception: 39 | return "THING" 40 | 41 | else: # query dbpedia about an ontology (property of a triple) 42 | try: 43 | results = sparql.query().convert() 44 | if len(results["results"]["bindings"]) == 0: 45 | return "*" 46 | else: # there should be exactly one result 47 | return results["results"]["bindings"][0]["x"]["value"][28:].upper() 48 | 49 | except Exception: 50 | return "*" 51 | 52 | 53 | def get_property_range(property): 54 | query_string = """ 55 | PREFIX rdfs: 56 | SELECT ?x 57 | WHERE { rdfs:range ?x .} 58 | """ 59 | 60 | return dbpedia_query(query_string) 61 | 62 | 63 | def get_property_domain(property): 64 | query_string = """ 65 | PREFIX rdfs: 66 | SELECT ?x 67 | WHERE { rdfs:domain ?x .} 68 | """ 69 | 70 | return dbpedia_query(query_string) 71 | 72 | 73 | def get_resource_type(resource): 74 | query_string = """ 75 | PREFIX rdf: 76 | SELECT ?x 77 | WHERE { rdf:type ?x .} 78 | """ 79 | 80 | return dbpedia_query(query_string, resource = True) 81 | -------------------------------------------------------------------------------- /utils/text_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Some useful function to process text. 3 | """ 4 | 5 | import difflib, re 6 | from dateutil import parser 7 | from nltk.tokenize import word_tokenize 8 | from nltk.tag import StanfordNERTagger 9 | 10 | TAGS = {'LOCATION', 'ORGANIZATION', 'DATE', \ 11 | 'MONEY', 'PERSON', 'PERCENT', 'TIME'} 12 | 13 | def initialize_tagger(DIR): 14 | """Given a path, return a StanfordNERTagger object.""" 15 | 16 | NERTagger = StanfordNERTagger( 17 | DIR + 'StanfordNLP/stanford-ner/classifiers/english.muc.7class.distsim.crf.ser.gz', 18 | DIR + 'StanfordNLP/stanford-ner/stanford-ner.jar', 19 | encoding='utf-8') 20 | 21 | return NERTagger 22 | 23 | 24 | def extract_named_entities(text): 25 | """ 26 | Given some text (string), return named entities and their positions (list). 27 | """ 28 | # tokenize 29 | tokenized_text = word_tokenize(text) 30 | # NER tagging 31 | NERTagger = initialize_tagger('/home/badr/') 32 | tagged_text = NERTagger.tag(tokenized_text) 33 | # entities 34 | named_entities = [] 35 | 36 | # set a previous_tag to non-entity 37 | previous_tag = 'O' 38 | 39 | for (word, tag) in tagged_text: 40 | if tag in TAGS: 41 | # if same as previous tag, concatenate with last entry 42 | if previous_tag == tag: 43 | named_entities.append(' '.join([named_entities.pop(), word])) 44 | 45 | else: # if not same as previous, append new entry 46 | named_entities.append(word) 47 | 48 | previous_tag = tag 49 | 50 | # get indicies of names entities in the text 51 | entity_position_list = [] 52 | 53 | for entity in named_entities: 54 | start_idx = text.find(entity) 55 | end_idx = start_idx + len(entity) 56 | entity_position_list.append((entity, (start_idx, end_idx))) 57 | 58 | return entity_position_list 59 | 60 | 61 | def find_ngrams(text, N=None): 62 | """Given some text, return list of ngrams from n = 1 up to N (or num of tokens).""" 63 | tokens = word_tokenize(text) 64 | ngrams = [] 65 | 66 | if N is None: 67 | N = len(tokens) 68 | 69 | for n in range(1, N + 1): 70 | ngrams.extend(*[zip(*[tokens[i:] for i in range(n)])]) 71 | 72 | return ngrams 73 | 74 | def tokenize_and_concat(text): 75 | """A wrapper for nltk tokenizer that returns a string.""" 76 | return ' '.join(word_tokenize(text)) 77 | 78 | 79 | def get_capitalized(text): 80 | """Return a list of capitalized words in a string.""" 81 | return [w for w in word_tokenize(text) if w[0].isupper()] 82 | 83 | 84 | def find_best_match(entity_str, text_lex): 85 | """ 86 | Given an entity string and list of ngrams that occur in the text 87 | lexicalization of a graph, return the ngram that is the best match. 88 | :para entity_str: entity (string) 89 | :para text_lex: text (string) 90 | """ 91 | lex_ngrams = [' '.join(ngrams) for ngrams in find_ngrams(text_lex)] 92 | 93 | best_matches = difflib.get_close_matches(entity_str, lex_ngrams) 94 | 95 | return best_matches[0] if best_matches else None 96 | 97 | 98 | def is_date_format(text): 99 | """Given a string, return True if date, False otherwise.""" 100 | try: 101 | parser.parse(text) 102 | return True 103 | except (ValueError, OverflowError) as e: 104 | return False 105 | 106 | def char_ngrams(chars, N=None): 107 | """Given some string, return list of character ngrams.""" 108 | char_ngrams = [] 109 | 110 | if N is None: 111 | N = len(chars) 112 | 113 | for n in range(2, N + 1): 114 | char_ngrams.extend(*[zip(*[chars[i:] for i in range(n)])]) 115 | 116 | return char_ngrams 117 | 118 | 119 | def generate_abbrs(text): 120 | """ 121 | Given a text (entity), return a list of possible abbreviations. 122 | For example, generate_abbrs("United States") -- > ['U. S.', 'U.S.', 'U S', 'US'] 123 | """ 124 | all_inits = [w[0] for w in text.split() if not w[0].isdigit()] 125 | capital_inits = [w[0].upper() for w in text.split() if not w[0].isdigit()] 126 | init_caps_only = [c for c in all_inits if c.isupper()] 127 | 128 | abbrs_pool = [] 129 | 130 | for char_set in (all_inits, capital_inits, init_caps_only): 131 | c_ngrams = [''.join(g) for g in char_ngrams(char_set)] + \ 132 | [' '.join(g) for g in char_ngrams(char_set)] + \ 133 | ['.'.join(g) + '.' for g in char_ngrams(char_set)] + \ 134 | ['.'.join(g) for g in char_ngrams(char_set)] + \ 135 | ['. '.join(g) + '.' for g in char_ngrams(char_set)] 136 | 137 | abbrs_pool.extend(c_ngrams) 138 | 139 | return sorted(set(abbrs_pool), key=len, reverse=True) 140 | 141 | 142 | # pre-compile regexes for the CamelCase converting function 143 | first_cap_re = re.compile('(.)([A-Z][a-z]+)') 144 | all_cap_re = re.compile('([a-z0-9])([A-Z])') 145 | 146 | def camel_case_split(property_string): 147 | """Return the text of the property after (camelCase) split.""" 148 | s1 = first_cap_re.sub(r'\1 \2', property_string) 149 | return all_cap_re.sub(r'\1 \2', s1).lower().replace('_', ' ').replace('/', ' ') 150 | 151 | # test function 152 | def test_date_format(): 153 | dates = [ 154 | "Jan 19, 1990", 155 | "January 19, 1990", 156 | "Jan 19,1990", 157 | "01/19/1990", 158 | "01/19/90", 159 | "1990", 160 | "Jan 1990", 161 | "January, 1990", 162 | "2009-6-19", 163 | "28th of August, 2008", 164 | "August 28th, 2008" 165 | ] 166 | 167 | for d in dates: 168 | print(d, is_date_format(d)) 169 | 170 | 171 | def test_abbrs(): 172 | print(generate_abbrs("Massachusetts Institute, of Technology, Sc.D. 1963")) 173 | print(generate_abbrs("United States")) 174 | print(generate_abbrs("New York City")) 175 | 176 | def test_NER(): 177 | text = """While in France, Christine Lagarde Johnson's team discussed 178 | short-term stimulus efforts in a recent interview with the Wall 179 | Street Journal on August 1st 2017. The United Nations decided to apply 180 | harmless nuclear regulations on Iran. The United States of America 181 | has many great cities such as New York, Las Vegas, and San Francisco. 182 | Distinguished Service Medal from United States Navy ranks higher than 183 | Department of Commerce Gold Medal.""".replace('\n', '') 184 | 185 | E = extract_named_entities(text) 186 | 187 | for (e, i) in E: 188 | print('§', e, '§', i[0], i[1]) 189 | 190 | 191 | def test_best_match(): 192 | entity_list = [ 193 | "Massachusetts Institute, of Technology, Sc.D. 1963", 194 | "Italy", 195 | "Colombian cuisine", 196 | "United States", 197 | """In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, 198 | Hessarghatta Main Road, Bangalore – 560090.""", 199 | "Dr. G. P. Prabhukumar", 200 | "Lars Løkke Rasmussen", 201 | "Prime Minister of Romania", 202 | "Türk Şehitleri Anıtı", "16000", 203 | "United States", 204 | "St. Louis", 205 | "25.0", 206 | "Americans", 207 | "Ilocano people", 208 | "English language", 209 | "Alan Shepard", 210 | "1971-07-01", 211 | "2014–15 Azerbaijan Premier League" 212 | ] 213 | 214 | text_list = [ 215 | """Born on the twentieth of January, 1930, 1963 Massachusetts Institute 216 | of Technology, Sc.D. graduate Buzz Aldrin has won 20 awards.""", 217 | """talian born Michele Marcolini is the manager of AC Lumezzane. 218 | He also played for Vicenza Calcio and owns Torino FC.""", 219 | """A typical dish found in Columbia is Bandeja paisa, which comes 220 | from the Antioquia Department region.""", 221 | """St Vincent-St Mary High School (Akron, Ohio, US) is the ground of 222 | Akron Summit Assault.""", 223 | """Acharya Institute of Technology (motto: 'Nurturing Excellence') was 224 | established in 2000 and is located at Soldevanahalli, Acharya Dr. 225 | Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 226 | 560090, Karnataka, India. The Institute is affiliated with 227 | Visvesvaraya Technological University of Belgaum.""", 228 | """The Acharya Institute of Technology's campus is located in 229 | Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta 230 | Main Road, Bangalore, India, 560090. It was established in 2000 and its 231 | director is Dr G.P. Prabhukumar. It is affiliated to the Visvesvaraya 232 | Technological UNiversity in Belgaum.""", 233 | """The School of Business and Social Sciences at the Aarhus University 234 | is located in the city of Aarhus, Denmark, and is affiliated with the 235 | European University Association in Brussels. Aarhus is ruled by a 236 | magistrate government, and is led by Lars Lokke Rasmussen. The 237 | religion is the Church of Denmark.""", 238 | """The 1 Decembrie 1918 University is located in Romania and its Latin 239 | name is 'Universitas Apulensis'. Romania's ethnic group is the Germans 240 | of Romania and its leader is Prime Minister Klaus Iohannis. Romania's 241 | capital is Bucharest and its anthem is Desteapta-te, romane!""", 242 | """Baku Turkish Martyrs' Memorial, made of red granite and white marble, 243 | is dedicated to the Ottoman Army soldiers killed in the Battle of Baku. 244 | The memorial, called Turk Sehitleri Aniti, was designed by Hüseyin 245 | Bütüner and Hilmi Güner and is located in Azerbaijan, the capital of 246 | which is Baku.""", 247 | """The School of Business and Social Sciences is located in Aarhus and 248 | it was established in 1928. It has 737 academic staff and 16,000 249 | students. Its dean is Thomas Pallesen and it is affiliated with the 250 | European University Association.""", 251 | """Andrews County Airport is located in Texas in the U.S. Austin is the 252 | capital of Texas whose largest city is Houston. English is spoken 253 | in that state.""", 254 | """Elliot See was born in Dallas on 23 July 1927. He graduated from the 255 | University of Texas at Austin which is an affiliate of the University 256 | of Texas system. He died in St louis .""", 257 | """Aarhus Airport, which serves the city of Aarhus in Denmark, has a 258 | runway length of 2,776 and is named 10R/28L. Aktieselskab operates the 259 | airport which is 25 metres above sea level.""", 260 | """The character of Baymax appeared in Big Hero 6 which stars Ryan 261 | Potter. He was created by Steven T Seagle and the American, 262 | Duncan Rouleau.""", 263 | """Batchoy is found in Philippine Spanish and Arabic speaking 264 | Philippines. The Philippines is home to the llocano and ENTITY.""", 265 | "English is the language used in 1634: The Ram Rebellion.", 266 | "Alan Shephard passed away on 1998-07-21 in California.", 267 | """Buzz Aldrin, who retired on July 1st 1971 , once spent 52 minutes 268 | in outer space.""", 269 | """AZAL Arena, which holds 3500 fans, is the ground of AZAL PFK who 270 | played in the Azerbaijan Premier League in 2014-15. Qarabag FK have 271 | been champions of this league. """ 272 | ] 273 | 274 | for (e, s) in zip(entity_list, text_list): 275 | best_match = find_best_match(e, s) 276 | print('entity:', e, ', best match:', best_match) 277 | 278 | def main(): 279 | # test extract_named_entities function. 280 | #test_NER() 281 | 282 | # test best match 283 | test_best_match() 284 | 285 | # test data format 286 | test_date_format() 287 | 288 | # test generate_abbrs 289 | test_abbrs() 290 | 291 | if __name__ == '__main__': 292 | main() 293 | --------------------------------------------------------------------------------