├── README.md
├── TransE.py
└── data
    ├── FB15k
        ├── entity2id.txt
        ├── relation2id.txt
        ├── test.txt
        ├── train.txt
        └── valid.txt
    └── WN18
        ├── entity2id.txt
        ├── relation2id.txt
        ├── test.txt
        ├── train.txt
        └── valid.txt


/README.md:
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 1 | # TransE
 2 | An implementation of TransE with tensorflow.
 3 | 
 4 | TransE is proposed by Antoine Bordes, Nicolas Usunier and Alberto Garcia-Duran in 2013. The paper title is [Translating Embeddings for Modeling Multi-relational Data](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf)
 5 | 
 6 | # Dataset
 7 | 
 8 | The dataset WN18 and FB15k are orginally published by TransE paper and cand be download [here](https://everest.hds.utc.fr/doku.php?id=en:transe)
 9 | 
10 | The data used here are from [here](https://github.com/thunlp/KB2E)
11 | 
12 | # Train and test
13 | 
14 | ## Run 
15 | 
16 | to run the model with default parameter setting: 
17 | 
18 | --- 'python3 TransE.py'. 
19 | 
20 | If you run this code using python2 there will be some error because print setting '\t' is not supported by python2. 
21 | 
22 | ##  change parameter setting
23 | 
24 | to change the parameter setting you can use 
25 | 
26 | --- 'python3 TransE.py --help' 
27 | 
28 | to see the optional arguments. 
29 | 
30 | ## test
31 | 
32 | the default setting for testing is testing once with 300 triples after every 10 training iteration
33 | 
34 | 
35 | 
36 | 


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/TransE.py:
--------------------------------------------------------------------------------
  1 | import tensorflow as tf 
  2 | import time 
  3 | import argparse
  4 | import random
  5 | import numpy as np 
  6 | import os.path
  7 | import math
  8 | import timeit
  9 | from multiprocessing import JoinableQueue, Queue, Process
 10 | from collections import defaultdict
 11 | 
 12 | class TransE:
 13 | 	@property
 14 | 	def variables(self):
 15 | 		return self.__variables
 16 | 
 17 | 	@property
 18 | 	def num_triple_train(self):
 19 | 		return self.__num_triple_train
 20 | 
 21 | 	@property 
 22 | 	def num_triple_test(self):
 23 | 		return self.__num_triple_test
 24 | 
 25 | 	@property
 26 | 	def testing_data(self):
 27 | 		return self.__triple_test
 28 | 
 29 | 	@property 
 30 | 	def num_entity(self):
 31 | 		return self.__num_entity
 32 | 
 33 | 	@property
 34 | 	def embedding_entity(self):
 35 | 		return self.__embedding_entity
 36 | 
 37 | 
 38 | 	@property
 39 | 	def embedding_relation(self):
 40 | 		return self.__embedding_relation
 41 | 
 42 | 	@property
 43 | 	def hr_t(self):
 44 | 		return self.__hr_t
 45 | 
 46 | 	@property 
 47 | 	def tr_h(self):
 48 | 		return self.__tr_h
 49 | 
 50 | 
 51 | 	def training_data_batch(self, batch_size = 512):
 52 | 		n_triple = len(self.__triple_train)
 53 | 		rand_idx = np.random.permutation(n_triple)
 54 | 		start = 0
 55 | 		while start < n_triple:
 56 | 			start_t = timeit.default_timer()
 57 | 			end = min(start+batch_size, n_triple)
 58 | 			size = end - start 
 59 | 			train_triple_positive = np.asarray([ self.__triple_train[x] for x in  rand_idx[start:end]])
 60 | 			train_triple_negative = []
 61 | 			for t in train_triple_positive:
 62 | 				replace_entity_id = np.random.randint(self.__num_entity)
 63 | 				random_num = np.random.random()
 64 | 
 65 | 				if self.__negative_sampling == 'unif':
 66 | 					replace_head_probability = 0.5
 67 | 				elif self.__negative_sampling == 'bern':
 68 | 					replace_head_probability = self.__relation_property[t[1]]
 69 | 				else:
 70 | 					raise NotImplementedError("Dose not support %s negative_sampling" %negative_sampling)
 71 | 
 72 | 				if random_num<replace_head_probability:
 73 | 					train_triple_negative.append((replace_entity_id, t[1],t[2]))
 74 | 				else:
 75 | 					train_triple_negative.append((t[0], t[1], replace_entity_id))
 76 | 				
 77 | 			start = end
 78 | 			prepare_t = timeit.default_timer()-start_t
 79 | 
 80 | 			yield train_triple_positive, train_triple_negative, prepare_t
 81 | 
 82 | 
 83 | 	def __init__(self, data_dir, negative_sampling,learning_rate, 
 84 | 				 batch_size, max_iter, margin, dimension, norm, evaluation_size, regularizer_weight):
 85 | 		# this part for data prepare
 86 | 		self.__data_dir=data_dir
 87 | 		self.__negative_sampling=negative_sampling
 88 | 		self.__regularizer_weight = regularizer_weight
 89 | 		self.__norm = norm
 90 | 
 91 | 		self.__entity2id={}
 92 | 		self.__id2entity={}
 93 | 		self.__relation2id={}
 94 | 		self.__id2relation={}
 95 | 
 96 | 		self.__triple_train=[] #[(head_id, relation_id, tail_id),...]
 97 | 		self.__triple_test=[]
 98 | 		self.__triple_valid=[]
 99 | 		self.__triple = []
100 | 
101 | 		self.__num_entity=0
102 | 		self.__num_relation=0
103 | 		self.__num_triple_train=0
104 | 		self.__num_triple_test=0
105 | 		self.__num_triple_valid=0
106 | 
107 | 		# load all the file: entity2id.txt, relation2id.txt, train.txt, test.txt, valid.txt
108 | 		self.load_data()
109 | 		print('finish preparing data. ')
110 | 
111 | 
112 | 		# this part for the model:
113 | 		self.__learning_rate = learning_rate
114 | 		self.__batch_size = batch_size
115 | 		self.__max_iter = max_iter
116 | 		self.__margin = margin
117 | 		self.__dimension = dimension
118 | 		self.__variables= []
119 | 		#self.__norm = norm
120 | 		self.__evaluation_size = evaluation_size
121 | 		bound = 6 / math.sqrt(self.__dimension)
122 | 		with tf.device('/cpu'):
123 | 			self.__embedding_entity = tf.get_variable('embedding_entity', [self.__num_entity, self.__dimension],
124 | 													   initializer=tf.random_uniform_initializer(minval=-bound, maxval=bound, seed = 123))
125 | 			self.__embedding_relation = tf.get_variable('embedding_relation', [self.__num_relation, self.__dimension],
126 | 														 initializer=tf.random_uniform_initializer(minval=-bound, maxval=bound, seed =124))
127 | 			self.__variables.append(self.__embedding_entity)
128 | 			self.__variables.append(self.__embedding_relation)
129 | 			print('finishing initializing')
130 | 		
131 | 
132 | 
133 | 	def load_data(self):
134 | 		print('loading entity2id.txt ...')
135 | 		with open(os.path.join(self.__data_dir, 'entity2id.txt')) as f:
136 | 			self.__entity2id = {line.strip().split('\t')[0]: int(line.strip().split('\t')[1]) for line in f.readlines()}
137 | 			self.__id2entity = {value:key for key,value in self.__entity2id.items()}
138 | 
139 | 
140 | 		with open(os.path.join(self.__data_dir,'relation2id.txt')) as f:
141 | 			self.__relation2id = {line.strip().split('\t')[0]: int(line.strip().split('\t')[1]) for line in f.readlines()}
142 | 			self.__id2relation = {value:key for key, value in self.__relation2id.items()}
143 | 
144 | 		def load_triple(self, triplefile):
145 | 			triple_list = [] #[(head_id, relation_id, tail_id),...]
146 | 			with open(os.path.join(self.__data_dir, triplefile)) as f:
147 | 				for line in f.readlines():
148 | 					line_list = line.strip().split('\t')
149 | 					assert len(line_list) == 3
150 | 					headid = self.__entity2id[line_list[0]]
151 | 					relationid = self.__relation2id[line_list[1]]
152 | 					tailid = self.__entity2id[line_list[2]]
153 | 					triple_list.append((headid, relationid, tailid))
154 | 					self.__hr_t[(headid, relationid)].add(tailid)
155 | 					self.__tr_h[(tailid, relationid)].add(headid)
156 | 			return triple_list
157 | 
158 | 		self.__hr_t = defaultdict(set)
159 | 		self.__tr_h = defaultdict(set)
160 | 		self.__triple_train = load_triple(self, 'train.txt')
161 | 		self.__triple_test = load_triple(self, 'test.txt')
162 | 		self.__triple_valid = load_triple(self, 'valid.txt')
163 | 		self.__triple = np.concatenate([self.__triple_train, self.__triple_test, self.__triple_valid], axis = 0 )
164 | 
165 | 		self.__num_relation = len(self.__relation2id)
166 | 		self.__num_entity = len(self.__entity2id)
167 | 		self.__num_triple_train = len(self.__triple_train)
168 | 		self.__num_triple_test = len(self.__triple_test)
169 | 		self.__num_triple_valid = len(self.__triple_valid)
170 | 
171 | 		print('entity number: ' + str(self.__num_entity))
172 | 		print('relation number: ' + str(self.__num_relation))
173 | 		print('training triple number: ' + str(self.__num_triple_train))
174 | 		print('testing triple number: ' + str(self.__num_triple_test))
175 | 		print('valid triple number: ' + str(self.__num_triple_valid))
176 | 
177 | 
178 | 		if self.__negative_sampling == 'bern':
179 | 			self.__relation_property_head = {x:[] for x in range(self.__num_relation)} #{relation_id:[headid1, headid2,...]}
180 | 			self.__relation_property_tail = {x:[] for x in range(self.__num_relation)} #{relation_id:[tailid1, tailid2,...]}
181 | 			for t in self.__triple_train:
182 | 				#print(t)
183 | 				self.__relation_property_head[t[1]].append(t[0])
184 | 				self.__relation_property_tail[t[1]].append(t[2])
185 | 			self.__relation_property = {x:(len(set(self.__relation_property_tail[x])))/(len(set(self.__relation_property_head[x]))+ len(set(self.__relation_property_tail[x]))) \
186 | 										 for x in self.__relation_property_head.keys()} # {relation_id: p, ...} 0< num <1, and for relation replace head entity with the property p
187 | 		else: 
188 | 			print("unif set do'n need to calculate hpt and tph")
189 | 
190 | 
191 | 
192 | 	def train(self, inputs):
193 | 		embedding_relation = self.__embedding_relation
194 | 		embedding_entity = self.__embedding_entity
195 | 
196 | 		triple_positive, triple_negative = inputs # triple_positive:(head_id,relation_id,tail_id)
197 | 
198 | 		norm_entity = tf.nn.l2_normalize(embedding_entity, dim = 1)
199 | 		norm_relation = tf.nn.l2_normalize(embedding_relation, dim = 1)
200 | 		norm_entity_l2sum = tf.sqrt(tf.reduce_sum(norm_entity**2, axis = 1))
201 | 
202 | 		embedding_positive_head = tf.nn.embedding_lookup(norm_entity, triple_positive[:, 0])
203 | 		embedding_positive_tail = tf.nn.embedding_lookup(norm_entity, triple_positive[:, 2])
204 | 		embedding_positive_relation = tf.nn.embedding_lookup(norm_relation, triple_positive[:, 1])
205 | 
206 | 		embedding_negative_head = tf.nn.embedding_lookup(norm_entity, triple_negative[:, 0])
207 | 		embedding_negative_tail = tf.nn.embedding_lookup(norm_entity, triple_negative[:, 2])
208 | 		embedding_negative_relation = tf.nn.embedding_lookup(norm_relation, triple_negative[:, 1])
209 | 
210 | 		score_positive = tf.reduce_sum(tf.abs(embedding_positive_head + embedding_positive_relation - embedding_positive_tail), axis = 1)
211 | 		score_negative = tf.reduce_sum(tf.abs(embedding_negative_head + embedding_negative_relation - embedding_negative_tail), axis = 1)
212 | 
213 | 		loss_every = tf.maximum(0., score_positive + self.__margin - score_negative)
214 | 		loss_triple = tf.reduce_sum(tf.maximum(0., score_positive + self.__margin - score_negative))
215 | 		self.__loss_regularizer = loss_regularizer = tf.reduce_sum(tf.abs(self.__embedding_relation)) + tf.reduce_sum(tf.abs(self.__embedding_entity))
216 | 		return loss_triple, loss_every, norm_entity_l2sum #+ loss_regularizer*self.__regularizer_weight
217 | 
218 | 
219 | 
220 | 	def test(self, inputs):
221 | 		embedding_relation = self.__embedding_relation
222 | 		embedding_entity = self.__embedding_entity
223 | 
224 | 		triple_test = inputs # (headid, tailid, tailid)
225 | 		head_vec = tf.nn.embedding_lookup(embedding_entity, triple_test[0])
226 | 		rel_vec = tf.nn.embedding_lookup(embedding_relation, triple_test[1])
227 | 		tail_vec = tf.nn.embedding_lookup(embedding_entity, triple_test[2])
228 | 
229 | 		norm_embedding_entity = tf.nn.l2_normalize(embedding_entity, dim =1 )
230 | 		norm_embedding_relation = tf.nn.l2_normalize(embedding_relation, dim = 1)
231 | 		norm_head_vec = tf.nn.embedding_lookup(norm_embedding_entity, triple_test[0])
232 | 		norm_rel_vec = tf.nn.embedding_lookup(norm_embedding_relation, triple_test[1])
233 | 		norm_tail_vec = tf.nn.embedding_lookup(norm_embedding_entity, triple_test[2])
234 | 
235 | 
236 | 		_, id_replace_head = tf.nn.top_k(tf.reduce_sum(tf.abs(embedding_entity + rel_vec - tail_vec), axis=1), k=self.__num_entity)
237 | 		_, id_replace_tail = tf.nn.top_k(tf.reduce_sum(tf.abs(head_vec + rel_vec - embedding_entity), axis=1), k=self.__num_entity)
238 | 
239 | 		_, norm_id_replace_head = tf.nn.top_k(tf.reduce_sum(tf.abs(norm_embedding_entity + norm_rel_vec - norm_tail_vec), axis=1), k=self.__num_entity)
240 | 		_, norm_id_replace_tail = tf.nn.top_k(tf.reduce_sum(tf.abs(norm_head_vec + norm_rel_vec - norm_embedding_entity), axis=1), k=self.__num_entity)
241 | 
242 | 
243 | 		
244 | 		return id_replace_head, id_replace_tail, norm_id_replace_head, norm_id_replace_tail
245 | 
246 | 		
247 | def train_operation(model, learning_rate=0.01, margin=1.0, optimizer_str = 'gradient'):
248 | 	with tf.device('/cpu'):
249 | 		train_triple_positive_input = tf.placeholder(tf.int32, [None, 3])
250 | 		train_triple_negative_input = tf.placeholder(tf.int32, [None, 3])
251 | 
252 | 		loss, loss_every, norm_entity = model.train([train_triple_positive_input, train_triple_negative_input])
253 | 		if optimizer_str == 'gradient':
254 | 			optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate)
255 | 		elif optimizer_str == 'rms':
256 | 			optimizer = tf.train.RMSPropOptimizer(learning_rate = learning_rate)
257 | 		elif optimizer_str == 'adam':
258 | 			optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
259 | 		else:
260 | 			raise NotImplementedError("Dose not support %s optimizer" %optimizer_str)
261 | 
262 | 		grads = optimizer.compute_gradients(loss, model.variables)
263 | 		op_train = optimizer.apply_gradients(grads)
264 | 
265 | 		return train_triple_positive_input, train_triple_negative_input, loss, op_train, loss_every, norm_entity
266 | 
267 | def test_operation(model):
268 | 	with tf.device('/cpu'):
269 | 		test_triple = tf.placeholder(tf.int32, [3])
270 | 		print('finish palceholder')
271 | 		head_rank, tail_rank, norm_head_rank, norm_tail_rank = model.test(test_triple)
272 | 		print('finish model.test')
273 | 		return test_triple, head_rank, tail_rank, norm_head_rank, norm_tail_rank
274 | 
275 | 
276 | def main():
277 | 	parser = argparse.ArgumentParser(description = "TransE")
278 | 	parser.add_argument('--data_dir', dest='data_dir', type=str, help='the directory of dataset', default='../Fb15k_withtext/')
279 | 	parser.add_argument('--learning_rate', dest='learning_rate', type=float, help='learning rate', default=0.01)
280 | 	parser.add_argument('--batch_size', dest='batch_size', type=int, help="batch size", default=4096)
281 | 	parser.add_argument('--max_iter', dest='max_iter', type=int, help='maximum interation', default=100)
282 | 	parser.add_argument('--optimizer', dest='optimizer', type=str, help='optimizer', default='adam')
283 | 	parser.add_argument('--dimension', dest='dimension', type=int, help='embedding dimension', default=50)
284 | 	parser.add_argument('--margin', dest='margin', type=float, help='margin', default=1.0)
285 | 	parser.add_argument('--norm', dest='norm', type=str, help='L1 or L2 norm', default='L1')
286 | 	parser.add_argument('--evaluation_size', dest='evaluation_size', type=int, help='batchsize for evaluation', default=500)
287 | 	parser.add_argument('--save_dir', dest='save_dir', type=str, help='directory to save tensorflow checkpoint directory', default='output/')
288 | 	parser.add_argument('--negative_sampling', dest='negative_sampling', type=str, help='choose unit or bern to generate negative examples', default='bern')
289 | 	parser.add_argument('--evaluate_per_iteration', dest='evaluate_per_iteration', type=int, help='evaluate the training result per x iteration', default=10)
290 | 	parser.add_argument('--evaluate_worker', dest='evaluate_worker', type=int, help='number of evaluate workers', default=4)
291 | 	parser.add_argument('--regularizer_weight', dest='regularizer_weight', type=float, help='regularization weight', default=1e-5)
292 | 	parser.add_argument('--n_test', dest = 'n_test', type=int, help='number of triples for test during the training', default = 300)
293 | 	args = parser.parse_args()
294 | 	print(args)
295 | 	model = TransE(negative_sampling=args.negative_sampling, data_dir=args.data_dir,
296 | 				   learning_rate=args.learning_rate, batch_size=args.batch_size,
297 | 				   max_iter=args.max_iter, margin=args.margin, 
298 | 				   dimension=args.dimension, norm=args.norm, evaluation_size=args.evaluation_size, 
299 | 				   regularizer_weight = args.regularizer_weight)
300 | 
301 | 	train_triple_positive_input, train_triple_negative_input, loss, op_train, loss_every, norm_entity = train_operation(model, learning_rate = args.learning_rate, margin = args.margin, optimizer_str = args.optimizer)
302 | 	test_triple, head_rank, tail_rank , norm_head_rank, norm_tail_rank= test_operation(model)
303 | 
304 | 
305 | 	with tf.Session() as session:
306 | 		tf.initialize_all_variables().run()
307 | 
308 | 		norm_rel = session.run(tf.nn.l2_normalize(model.embedding_relation, dim =1))
309 | 		session.run(tf.assign(model.embedding_relation, norm_rel))
310 | 		norm_ent = session.run(tf.nn.l2_normalize(model.embedding_entity, dim =1))
311 | 		session.run(tf.assign(model.embedding_entity, norm_ent))
312 | 
313 | 		for n_iter in range(args.max_iter):
314 | 			accu_loss =0.
315 | 			batch = 0
316 | 			num_batch = model.num_triple_train/args.batch_size
317 | 			start_time = timeit.default_timer()
318 | 			prepare_time = 0.
319 | 			
320 | 			for tp, tn , t in  model.training_data_batch(batch_size= args.batch_size):
321 | 				l, _, l_every, norm_e = session.run([loss, op_train, loss_every, norm_entity], {train_triple_positive_input:tp, train_triple_negative_input: tn})
322 | 				accu_loss += l
323 | 				batch += 1
324 | 				print('[%.2f sec](%d/%d): -- loss: %.5f' %(timeit.default_timer()-start_time, batch, num_batch , l), end='\r')
325 | 				prepare_time += t
326 | 			print('iter[%d] ---loss: %.5f ---time: %.2f ---prepare time : %.2f' %(n_iter, accu_loss, timeit.default_timer()-start_time, prepare_time))
327 | 			
328 | 			if n_iter %args.evaluate_per_iteration == 0 or n_iter ==0 or n_iter == args.max_iter-1:
329 | 				#print("[iter %d] after l2 normalization the entity vectors: %s"%(n_iter, str(norm_e[:10])))
330 | 				#print("[iter %d] after training the entity vectors: %s"%(n_iter, str(session.run(tf.sqrt(tf.reduce_sum(model.embedding_entity**2, axis = 1))[:10]))))
331 | 
332 | 				rank_head = []
333 | 				rank_tail = []
334 | 				filter_rank_head = []
335 | 				filter_rank_tail = []
336 | 
337 | 				norm_rank_head = []
338 | 				norm_rank_tail = []
339 | 				norm_filter_rank_head = []
340 | 				norm_filter_rank_tail = []
341 | 
342 | 				start = timeit.default_timer()
343 | 				testing_data = model.testing_data
344 | 				hr_t = model.hr_t
345 | 				tr_h = model.tr_h
346 | 				n_test = args.n_test
347 | 				if n_iter == args.max_iter-1:	n_test = model.num_triple_test
348 | 				for i in range(n_test):
349 | 					print('[%.2f sec] --- testing[%d/%d]' %(timeit.default_timer()-start, i+1, n_test), end='\r')
350 | 					t = testing_data[i]
351 | 					id_replace_head , id_replace_tail, norm_id_replace_head , norm_id_replace_tail  = session.run([head_rank, tail_rank, norm_head_rank, norm_tail_rank], {test_triple:t})
352 | 					hrank = 0
353 | 					fhrank = 0
354 | 					for i in range(len(id_replace_head)):
355 | 						val = id_replace_head[-i-1]
356 | 						if val == t[0]:
357 | 							break						
358 | 						else: 
359 | 							hrank += 1
360 | 							fhrank += 1 
361 | 							if val in tr_h[(t[2],t[1])]:
362 | 								fhrank -= 1
363 | 
364 | 					norm_hrank = 0
365 | 					norm_fhrank = 0
366 | 					for i in range(len(norm_id_replace_head)):
367 | 						val = norm_id_replace_head[-i-1]
368 | 						if val == t[0]:
369 | 							break						
370 | 						else: 
371 | 							norm_hrank += 1
372 | 							norm_fhrank += 1 
373 | 							if val in tr_h[(t[2],t[1])]:
374 | 								norm_fhrank -= 1
375 | 									
376 | 
377 | 					trank = 0
378 | 					ftrank = 0
379 | 					for i in range(len(id_replace_tail)):
380 | 						val = id_replace_tail[-i-1]
381 | 						if val == t[2]:
382 | 							break
383 | 						else:
384 | 							trank += 1
385 | 							ftrank += 1
386 | 							if val in hr_t[(t[0], t[1])]:
387 | 								ftrank -= 1
388 | 
389 | 					norm_trank = 0
390 | 					norm_ftrank = 0
391 | 					for i in range(len(norm_id_replace_tail)):
392 | 						val = norm_id_replace_tail[-i-1]
393 | 						if val == t[2]:
394 | 							break
395 | 						else:
396 | 							norm_trank += 1
397 | 							norm_ftrank += 1
398 | 							if val in hr_t[(t[0], t[1])]:
399 | 								norm_ftrank -= 1
400 | 
401 | 					rank_head.append(hrank)
402 | 					rank_tail.append(trank)
403 | 					filter_rank_head.append(fhrank)
404 | 					filter_rank_tail.append(ftrank)
405 | 
406 | 					norm_rank_head.append(norm_hrank)
407 | 					norm_rank_tail.append(norm_trank)
408 | 					norm_filter_rank_head.append(norm_fhrank)
409 | 					norm_filter_rank_tail.append(norm_ftrank)
410 | 
411 | 				mean_rank_head = np.sum(rank_head, dtype=np.float32)/n_test
412 | 				mean_rank_tail = np.sum(rank_tail, dtype=np.float32)/n_test
413 | 				filter_mean_rank_head = np.sum(filter_rank_head, dtype=np.float32)/n_test
414 | 				filter_mean_rank_tail = np.sum(filter_rank_tail, dtype=np.float32)/n_test
415 | 
416 | 				norm_mean_rank_head = np.sum(norm_rank_head, dtype=np.float32)/n_test
417 | 				norm_mean_rank_tail = np.sum(norm_rank_tail, dtype=np.float32)/n_test
418 | 				norm_filter_mean_rank_head = np.sum(norm_filter_rank_head, dtype=np.float32)/n_test
419 | 				norm_filter_mean_rank_tail = np.sum(norm_filter_rank_tail, dtype=np.float32)/n_test
420 | 
421 | 				hit10_head = np.sum(np.asarray(np.asarray(rank_head)<10 , dtype=np.float32))/n_test
422 | 				hit10_tail = np.sum(np.asarray(np.asarray(rank_tail)<10 , dtype=np.float32))/n_test
423 | 				filter_hit10_head = np.sum(np.asarray(np.asarray(filter_rank_head)<10 , dtype=np.float32))/n_test
424 | 				filter_hit10_tail = np.sum(np.asarray(np.asarray(filter_rank_tail)<10 , dtype=np.float32))/n_test
425 | 
426 | 				norm_hit10_head = np.sum(np.asarray(np.asarray(norm_rank_head)<10 , dtype=np.float32))/n_test
427 | 				norm_hit10_tail = np.sum(np.asarray(np.asarray(norm_rank_tail)<10 , dtype=np.float32))/n_test
428 | 				norm_filter_hit10_head = np.sum(np.asarray(np.asarray(norm_filter_rank_head)<10 , dtype=np.float32))/n_test
429 | 				norm_filter_hit10_tail = np.sum(np.asarray(np.asarray(norm_filter_rank_tail)<10 , dtype=np.float32))/n_test
430 | 
431 | 				print('iter:%d --mean rank: %.2f --hit@10: %.2f' %(n_iter, (mean_rank_head+ mean_rank_tail)/2, (hit10_tail+hit10_head)/2))
432 | 				print('iter:%d --filter mean rank: %.2f --filter hit@10: %.2f' %(n_iter, (filter_mean_rank_head+ filter_mean_rank_tail)/2, (filter_hit10_tail+filter_hit10_head)/2))
433 | 
434 | 				print('iter:%d --norm mean rank: %.2f --norm hit@10: %.2f' %(n_iter, (norm_mean_rank_head+ norm_mean_rank_tail)/2, (norm_hit10_tail+norm_hit10_head)/2))
435 | 				print('iter:%d --norm filter mean rank: %.2f --norm filter hit@10: %.2f' %(n_iter, (norm_filter_mean_rank_head+ norm_filter_mean_rank_tail)/2, (norm_filter_hit10_tail+norm_filter_hit10_head)/2))
436 | 			
437 | 
438 | if __name__ =="__main__":
439 | 	main()


--------------------------------------------------------------------------------
/data/FB15k/relation2id.txt:
--------------------------------------------------------------------------------
   1 | /people/appointed_role/appointment./people/appointment/appointed_by	0
   2 | /location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency	1
   3 | /tv/tv_series_episode/guest_stars./tv/tv_guest_role/actor	2
   4 | /music/performance_role/track_performances./music/track_contribution/contributor	3
   5 | /medicine/disease/prevention_factors	4
   6 | /organization/organization_member/member_of./organization/organization_membership/organization	5
   7 | /american_football/football_player/receiving./american_football/player_receiving_statistics/season	6
   8 | /user/narphorium/people/wealthy_person/net_worth./measurement_unit/dated_money_value/currency	7
   9 | /sports/sports_team/roster./soccer/football_roster_position/player	8
  10 | /business/company_type/companies_of_this_type	9
  11 | /tv/tv_program/regular_cast./tv/regular_tv_appearance/character	10
  12 | /architecture/structure/address./location/mailing_address/citytown	11
  13 | /base/popstra/religion/membership./base/popstra/religion_choice/member	12
  14 | /base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/position	13
  15 | /government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office	14
  16 | /computer/programming_language/influenced_by	15
  17 | /base/folklore/mythology/mythical_creatures	16
  18 | /fictional_universe/fictional_character/species	17
  19 | /cvg/game_character/games./cvg/game_performance/game	18
  20 | /education/field_of_study/students_majoring./education/education/minor	19
  21 | /sports/sports_team/roster./american_football/football_historical_roster_position/player	20
  22 | /people/cause_of_death/includes_causes_of_death	21
  23 | /base/onephylogeny/type_of_thing/includes	22
  24 | /sports/sports_team/roster./basketball/basketball_roster_position/player	23
  25 | /film/film/runtime./film/film_cut/type_of_film_cut	24
  26 | /astronomy/star_system/planetary_system	25
  27 | /film/actor/dubbing_performances./film/dubbing_performance/language	26
  28 | /base/popstra/organization/supporter./base/popstra/support/supporter	27
  29 | /media_common/dedicatee/dedications./media_common/dedication/dedicated_by	28
  30 | /sports/pro_athlete/teams./american_football/football_historical_roster_position/team	29
  31 | /education/field_of_study/students_majoring./education/education/major_field_of_study	30
  32 | /biology/animal_breed/place_of_origin	31
  33 | /film/film/starring./film/performance/special_performance_type	32
  34 | /media_common/media_genre/child_genres	33
  35 | /base/popstra/celebrity/shops_at./base/popstra/shopping_choice/company	34
  36 | /finance/currency/countries_used	35
  37 | /visual_art/visual_art_genre/artworks	36
  38 | /location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division	37
  39 | /base/x2010fifaworldcupsouthafrica/world_cup_participant/world_cup_team./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/position	38
  40 | /food/diet/followers	39
  41 | /tv/special_tv_performance_type/starring_performances./tv/regular_tv_appearance/character	40
  42 | /american_football/football_player/former_teams./american_football/football_historical_roster_position/position_s	41
  43 | /people/marriage_union_type/unions_of_this_type./people/marriage/spouse	42
  44 | /organization/organization_sector/organizations_in_this_sector	43
  45 | /baseball/baseball_team/team_stats./baseball/baseball_team_stats/season	44
  46 | /award/award_category/disciplines_or_subjects	45
  47 | /base/yalebase/secret_society_member/belonged_to./base/yalebase/secret_society_membership/society	46
  48 | /government/governmental_body/body_this_is_a_component_of	47
  49 | /people/person/parents	48
  50 | /user/jg/default_domain/olympic_games/sports	49
  51 | /medicine/disease/includes_diseases	50
  52 | /biology/animal_breed/breed_of	51
  53 | /olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/sport	52
  54 | /people/person/nationality	53
  55 | /base/biblioness/bibs_location/state	54
  56 | /ice_hockey/hockey_player/current_team./sports/sports_team_roster/position	55
  57 | /location/statistical_region/rent50_3./measurement_unit/dated_money_value/currency	56
  58 | /cvg/computer_videogame/prequel	57
  59 | /music/genre/parent_genre	58
  60 | /location/country/languages_spoken	59
  61 | /film/special_film_performance_type/film_performance_type./film/performance/actor	60
  62 | /base/thoroughbredracing/thoroughbred_racehorse_sex/horses_of_this_sex	61
  63 | /tv/tv_genre/programs	62
  64 | /base/thoroughbredracing/thoroughbred_racehorse_color/horses_of_this_color	63
  65 | /music/group_member/membership./music/group_membership/group	64
  66 | /film/film_genre/films_in_this_genre	65
  67 | /american_football/football_player/games./american_football/player_game_statistics/season	66
  68 | /baseball/baseball_player/current_team./sports/sports_team_roster/position	67
  69 | /fictional_universe/fictional_universe/characters	68
  70 | /interests/collection_category/parent_category	69
  71 | /business/consumer_product/company./business/company_product_relationship/company	70
  72 | /sports/sports_position/players./american_football/football_historical_roster_position/team	71
  73 | /film/film/film_art_direction_by	72
  74 | /base/locations/countries/continent	73
  75 | /music/artist/label	74
  76 | /theater/theater_actor/theater_roles./theater/theater_role/role	75
  77 | /people/cause_of_death/people	76
  78 | /tv/tv_network/areas_served	77
  79 | /base/aareas/schema/administrative_area/administrative_parent	78
  80 | /book/book_subject/works	79
  81 | /base/infrastructure/power_station/fuel_type	80
  82 | /projects/project_participant/projects./projects/project_participation/role	81
  83 | /medicine/drug_class/drugs	82
  84 | /soccer/football_position/players	83
  85 | /location/statistical_region/population./measurement_unit/dated_integer/source	84
  86 | /olympics/olympic_participating_country/olympics_participated_in	85
  87 | /common/annotation_category/annotations./common/webpage/topic	86
  88 | /user/alexbl/honorary_title/titled_person/honorary_title./user/alexbl/honorary_title/honorary_title_holding/honorary_title	87
  89 | /food/dietary_restriction/incompatible_ingredients	88
  90 | /film/film_crewmember/films_crewed./film/film_crew_gig/film	89
  91 | /fictional_universe/fictional_character/place_of_birth	90
  92 | /base/argumentmaps/morally_disputed_activity/instance_of_abstract_moral_dispute	91
  93 | /film/film/other_film_companies./film/film_film_company_relationship/film_company	92
  94 | /music/genre/artists	93
  95 | /ice_hockey/hockey_player/current_team./ice_hockey/hockey_roster_position/team	94
  96 | /location/country/first_level_divisions	95
  97 | /baseball/baseball_position/players	96
  98 | /broadcast/artist/content	97
  99 | /government/political_party/politicians_in_this_party./government/political_party_tenure/politician	98
 100 | /basketball/basketball_team/roster./basketball/basketball_roster_position/player	99
 101 | /olympics/olympic_athlete/medals_won./olympics/olympic_medal_honor/country	100
 102 | /location/location/street_address./location/mailing_address/state_province_region	101
 103 | /fictional_universe/character_powers/characters_with_this_ability	102
 104 | /medicine/medical_treatment/used_to_treat	103
 105 | /people/person/education./education/education/institution	104
 106 | /architecture/structure/address./location/mailing_address/state_province_region	105
 107 | /sports/sports_position/players./soccer/football_roster_position/player	106
 108 | /business/business_operation/cash./measurement_unit/dated_money_value/currency	107
 109 | /royalty/noble_person/titles./royalty/noble_title_tenure/noble_title	108
 110 | /base/newsevents/news_reported_event/news_report_s./base/newsevents/news_report/news_reporting_organisation	109
 111 | /base/fight/crime_type/includes_crimes	110
 112 | /tv/tv_series_episode/producers./tv/tv_producer_episode_credit/producer	111
 113 | /film/personal_film_appearance_type/film_appearances./film/personal_film_appearance/film	112
 114 | /comic_books/comic_book_series/created_by	113
 115 | /base/patronage/patron/related_client./base/patronage/patron_client_relationship/client	114
 116 | /base/parody/parody/parody_of	115
 117 | /media_common/dedicator/dedications./media_common/dedication/dedicated_to	116
 118 | /broadcast/radio_station/owner	117
 119 | /soccer/football_player/current_team./sports/sports_team_roster/team	118
 120 | /military/military_conflict/commanders./military/military_command/military_combatant	119
 121 | /people/deceased_person/place_of_burial	120
 122 | /sports/pro_athlete/teams./american_football/football_roster_position/team	121
 123 | /fictional_universe/fictional_character_creator/fictional_characters_created	122
 124 | /tv/special_tv_performance_type/starring_performances./tv/regular_tv_appearance/actor	123
 125 | /music/artist/supporting_artists	124
 126 | /transportation/bridge/locale	125
 127 | /music/artist/contribution./music/recording_contribution/performance_role	126
 128 | /medicine/drug/legal_status	127
 129 | /book/literary_series/author_s	128
 130 | /tv/tv_actor/starring_roles./tv/regular_tv_appearance/character	129
 131 | /ice_hockey/hockey_team/current_roster./ice_hockey/hockey_roster_position/player	130
 132 | /base/contractbridge/bridge_player/second_place_finish./base/contractbridge/bridge_tournament_standings/second_place	131
 133 | /award/award/presented_by	132
 134 | /location/neighborhood/neighborhood_of	133
 135 | /base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months	134
 136 | /architecture/architect/structures_designed	135
 137 | /award/award_nominated_work/award_nominations./award/award_nomination/award	136
 138 | /food/diet_follower/follows_diet	137
 139 | /religion/religion/places_of_worship	138
 140 | /travel/travel_destination/how_to_get_here./travel/transportation/transport_operator	139
 141 | /education/educational_institution_campus/educational_institution	140
 142 | /book/author/series_written_or_contributed_to	141
 143 | /ice_hockey/hockey_player/current_team./ice_hockey/hockey_roster_position/position	142
 144 | /award/award_winner/awards_won./award/award_honor/award_winner	143
 145 | /medicine/disease/risk_factors	144
 146 | /baseball/baseball_team/current_roster./sports/sports_team_roster/player	145
 147 | /government/political_district/representatives./government/government_position_held/basic_title	146
 148 | /base/ecology/food_web_member/belongs_to_ecosystem_s	147
 149 | /people/ethnicity/geographic_distribution	148
 150 | /music/conducted_ensemble/conductors./music/conducting_tenure/conductor	149
 151 | /medicine/disease/treatments	150
 152 | /base/popstra/celebrity/hangout./base/popstra/hangout/business_location	151
 153 | /sports/pro_athlete/sports_played_professionally./sports/pro_sports_played/sport	152
 154 | /architecture/museum/address./location/mailing_address/citytown	153
 155 | /biology/organism_classification/rank	154
 156 | /geography/lake_type/lakes_of_this_type	155
 157 | /olympics/olympic_athlete/medals_won./olympics/olympic_medal_honor/olympics	156
 158 | /people/family_member/family	157
 159 | /base/petbreeds/dog_coat_color/dog_breeds	158
 160 | /tv/tv_program/regular_cast./tv/regular_tv_appearance/seasons	159
 161 | /tv/tv_program/genre	160
 162 | /theater/play/country_of_origin	161
 163 | /olympics/olympic_athlete/medals_won./olympics/olympic_medal_honor/medal	162
 164 | /sports/sports_position/players./sports/sports_team_roster/position	163
 165 | /cvg/game_voice_actor/computer_game_voice_performances./cvg/game_performance/character	164
 166 | /biology/animal_breed/registered_with./biology/breed_registration/registry	165
 167 | /sports/sports_league/teams./sports/sports_league_participation/team	166
 168 | /sports/sports_position/players./baseball/baseball_roster_position/team	167
 169 | /military/rank/used_by_services./military/military_service/military_force	168
 170 | /fictional_universe/fictional_character/gender	169
 171 | /education/school/highest_grade_taught	170
 172 | /education/educational_degree/people_with_this_degree./education/education/minor	171
 173 | /baseball/baseball_team/league	172
 174 | /base/x2010fifaworldcupsouthafrica/world_cup_participant/world_cup_team./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/national_team	173
 175 | /military/military_conflict/casualties./military/casualties/combatant	174
 176 | /computer/software/software_genre	175
 177 | /military/armed_force/personnel./military/military_service/rank	176
 178 | /royalty/system_of_nobility/titles./royalty/system_title_relationship/title	177
 179 | /basketball/basketball_team/roster./sports/sports_team_roster/position	178
 180 | /base/disaster2/tactic/attacks_of_this_form./base/disaster2/attack_process/attack_event	179
 181 | /people/person/place_of_birth	180
 182 | /cvg/game_series/games_in_series	181
 183 | /spaceflight/space_agency/astronauts	182
 184 | /time/event/includes_event	183
 185 | /base/aareas/schema/administrative_area/capital	184
 186 | /baseball/baseball_player/former_teams./baseball/baseball_historical_roster_position/team	185
 187 | /medicine/icd_9_cm_classification/parent_classification	186
 188 | /people/cause_of_death/parent_cause_of_death	187
 189 | /education/university/domestic_tuition./measurement_unit/dated_money_value/currency	188
 190 | /people/profession/specialization_of	189
 191 | /music/musical_group/member./music/group_membership/role	190
 192 | /media_common/literary_genre/books_in_this_genre	191
 193 | /sports/sports_team/roster./soccer/football_roster_position/position	192
 194 | /music/instrument/instrumentalists	193
 195 | /government/politician/government_positions_held./government/government_position_held/appointed_by	194
 196 | /sports/sports_team/colors	195
 197 | /religion/religious_text/religious_text_of	196
 198 | /organization/role/leaders./organization/leadership/person	197
 199 | /fictional_universe/fictional_setting/fictional_characters_born_here	198
 200 | /soccer/football_player/current_team./soccer/football_roster_position/team	199
 201 | /film/production_company/films	200
 202 | /sports/sports_award_winner/awards./sports/sports_award/award	201
 203 | /base/argumentmaps/abstract_moral_dispute/particular_instances	202
 204 | /people/person/sibling_s./people/sibling_relationship/sibling	203
 205 | /influence/influence_node/influenced	204
 206 | /book/newspaper_owner/newspapers_owned	205
 207 | /geography/river/basin_countries	206
 208 | /film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/film	207
 209 | /royalty/order_of_chivalry/recipients./royalty/chivalric_order_membership/recipient	208
 210 | /visual_art/art_period_movement/associated_artists	209
 211 | /film/film/dubbing_performances./film/dubbing_performance/actor	210
 212 | /user/szaijan/fantasy_football/player/nfl_team	211
 213 | /base/contractbridge/bridge_player/second_place_finish./base/contractbridge/bridge_tournament_standings/first_place	212
 214 | /military/armed_force/personnel./military/military_service/military_person	213
 215 | /organization/organization/board_members./organization/organization_board_membership/role	214
 216 | /location/it_province/capital	215
 217 | /american_football/football_team/current_roster./american_football/football_roster_position/position	216
 218 | /base/contractbridge/bridge_player/first_place_finish./base/contractbridge/bridge_tournament_standings/second_place	217
 219 | /common/webpage/category	218
 220 | /medicine/type_of_infectious_agent/diseases	219
 221 | /book/newspaper/owner	220
 222 | /architecture/building/building_function	221
 223 | /time/recurring_event/instances	222
 224 | /base/crime/crime_victim/crime	223
 225 | /american_football/football_coach/coaching_history./american_football/football_historical_coach_position/team	224
 226 | /sports/sports_league_draft/picks./sports/sports_league_draft_pick/team	225
 227 | /base/contractbridge/bridge_player/teammates./base/contractbridge/bridge_player_teammates/teammate	226
 228 | /architecture/architectural_style/examples	227
 229 | /cvg/computer_videogame/characters./cvg/game_performance/voice_actor	228
 230 | /olympics/olympic_games/athletes./olympics/olympic_athlete_affiliation/sport	229
 231 | /internet/website_owner/websites_owned_new./internet/website_ownership/website	230
 232 | /olympics/olympic_athlete/country./olympics/olympic_athlete_affiliation/olympics	231
 233 | /film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue	232
 234 | /base/biblioness/bibs_location/country	233
 235 | /organization/organization/spun_off_from./organization/organization_spin_off/parent_company	234
 236 | /government/government_office_or_title/category	235
 237 | /aviation/airport/hub_for	236
 238 | /cvg/cvg_publisher/games_published	237
 239 | /government/legislative_session/members./government/government_position_held/basic_title	238
 240 | /base/wrestling/solo_wrestler_or_team/members	239
 241 | /government/government_office_or_title/office_holders./government/government_position_held/appointed_by	240
 242 | /tv/tv_actor/starring_roles./tv/regular_tv_appearance/series	241
 243 | /education/educational_institution/students_graduates./education/education/major_field_of_study	242
 244 | /user/alexander/misc/murdered_person/place_murdered	243
 245 | /base/saints/saint/venerated_in	244
 246 | /film/film/gross_revenue./measurement_unit/dated_money_value/currency	245
 247 | /sports/sports_position/players./soccer/football_roster_position/team	246
 248 | /film/film_character/portrayed_in_films./film/performance/special_performance_type	247
 249 | /organization/organization/leadership./organization/leadership/role	248
 250 | /base/nobelprizes/nobel_subject_area/nobel_awards./base/nobelprizes/nobel_honor/nobel_prize_winner	249
 251 | /tv/special_tv_performance_type/episode_performances./tv/tv_guest_role/actor	250
 252 | /base/americancomedy/celebrity_impressionist/celebrities_impersonated	251
 253 | /base/activism/organization/area_of_activism	252
 254 | /organization/organization/involved_in_merger./organization/organization_merger/became_organization	253
 255 | /fictional_universe/character_gender/characters_of_this_gender	254
 256 | /people/ethnicity/included_in_group	255
 257 | /baseball/baseball_player/position_s	256
 258 | /american_football/football_coach_position/coaches_holding_this_position./american_football/football_historical_coach_position/team	257
 259 | /base/popstra/restaurant/restaurant_choice./base/popstra/restaurant_choice/diner	258
 260 | /baseball/baseball_team/current_roster./sports/sports_team_roster/position	259
 261 | /base/popstra/company/fashion_choice./base/popstra/fashion_choice/fashion_wearer	260
 262 | /location/country/second_level_divisions	261
 263 | /sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/player	262
 264 | /ice_hockey/hockey_player/former_team_s./ice_hockey/hockey_previous_roster_position/team	263
 265 | /fictional_universe/fictional_character/ethnicity	264
 266 | /sports/sports_team/location	265
 267 | /soccer/football_player/goals_scored./soccer/football_goal/point_awarded_to	266
 268 | /base/onephylogeny/type_of_thing/things_of_this_type	267
 269 | /music/voice/singers	268
 270 | /film/film/distributors./film/film_film_distributor_relationship/distributor	269
 271 | /religion/religion/notable_figures	270
 272 | /influence/influence_node/peers./influence/peer_relationship/peers	271
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1139 | /people/person/gender	1138
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1142 | /language/language_dialect/language	1141
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1149 | /biology/breed_group/breeds_in_this_group./biology/breed_registration/breed	1148
1150 | /tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person	1149
1151 | /film/film_job/films_with_this_crew_job./film/film_crew_gig/crewmember	1150
1152 | /internet/website/category	1151
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1154 | /basketball/basketball_player/team./sports/sports_team_roster/team	1153
1155 | /location/location/nearby_airports	1154
1156 | /sports/pro_athlete/teams./basketball/basketball_roster_position/position	1155
1157 | /ice_hockey/hockey_team/current_roster./ice_hockey/hockey_roster_position/position	1156
1158 | /sports/sports_league_draft/picks./sports/sports_league_draft_pick/school	1157
1159 | /government/government_agency/jurisdiction	1158
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1161 | /base/popstra/celebrity/religion./base/popstra/religion_choice/religion	1160
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1167 | /baseball/baseball_player/current_team./baseball/baseball_roster_position/team	1166
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1181 | /language/human_language/countries_spoken_in	1180
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1183 | /user/robert/us_congress/us_representative/congressional_caucus_memberships	1182
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1185 | /basketball/basketball_player/position_s	1184
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1187 | /sports/sport/leagues	1186
1188 | /book/periodical/subjects	1187
1189 | /martial_arts/martial_art/well_known_practitioner	1188
1190 | /education/academic/advisees	1189
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1193 | /music/group_member/artists_supported	1192
1194 | /architecture/type_of_museum/museums	1193
1195 | /sports/sports_team/sport	1194
1196 | /fictional_universe/fictional_character/married_to./fictional_universe/marriage_of_fictional_characters/spouses	1195
1197 | /education/educational_institution/sexes_accepted./education/gender_enrollment/sex	1196
1198 | /film/actor/dubbing_performances./film/dubbing_performance/character	1197
1199 | /tv/tv_actor/guest_roles./tv/tv_guest_role/special_performance_type	1198
1200 | /venture_capital/venture_funded_company/venture_investors./venture_capital/venture_investment/investor	1199
1201 | /symbols/name_source/namesakes	1200
1202 | /award/award_winning_work/awards_won./award/award_honor/award_winner	1201
1203 | /travel/transport_terminus/travel_destinations_served./travel/transportation/transport_terminus	1202
1204 | /business/business_operation/operating_income./measurement_unit/dated_money_value/currency	1203
1205 | /base/crime/crime_victim/crime_type	1204
1206 | /media_common/dedicated_work/dedication./media_common/dedication/dedicated_by	1205
1207 | /cvg/game_voice_actor/computer_game_voice_performances./cvg/game_performance/game	1206
1208 | /education/educational_degree/people_with_this_degree./education/education/institution	1207
1209 | /military/military_combatant/casualties./military/casualties/military_conflict	1208
1210 | /broadcast/broadcast/content	1209
1211 | /organization/organization/companies_acquired./business/acquisition/company_acquired	1210
1212 | /comic_books/comic_book_series/publisher	1211
1213 | /religion/religion/organizations	1212
1214 | /film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance	1213
1215 | /base/americancomedy/impersonated_celebrity/impersonated_by	1214
1216 | /base/aareas/schema/administrative_area/administrative_area_type	1215
1217 | /tv/tv_program/seasons	1216
1218 | /sports/sports_team/roster./ice_hockey/hockey_roster_position/player	1217
1219 | /military/military_combatant/military_conflicts./military/military_combatant_group/conflict	1218
1220 | /business/consumer_company/products./business/company_product_relationship/consumer_product	1219
1221 | /business/asset_owner/assets_owned./business/asset_ownership/owned_asset	1220
1222 | /dining/restaurant/cuisine	1221
1223 | /user/alexander/philosophy/philosopher/era	1222
1224 | /metropolitan_transit/transit_line/service_type	1223
1225 | /fictional_universe/fictional_character/parents	1224
1226 | /protected_sites/site_listing_category/listed_sites./protected_sites/natural_or_cultural_site_listing/listed_site	1225
1227 | /film/actor/film./film/performance/film	1226
1228 | /food/food/nutrients./food/nutrition_fact/nutrient	1227
1229 | /geography/body_of_water/islands	1228
1230 | /baseball/baseball_team/current_roster./baseball/baseball_roster_position/player	1229
1231 | /sports/sport/officials	1230
1232 | /education/school_district/schools	1231
1233 | /base/schemastaging/tv_character_extra/regular_dubbing_performances./base/schemastaging/tv_star_dubbing_performance/program	1232
1234 | /religion/place_of_worship/religion	1233
1235 | /aviation/airliner_accident/flight_destination	1234
1236 | /education/field_of_study/academics_in_this_field	1235
1237 | /fictional_universe/fictional_universe/works_set_here	1236
1238 | /base/popstra/celebrity/wears./base/popstra/fashion_choice/designer	1237
1239 | /ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position	1238
1240 | /broadcast/radio_station_owner/radio_stations	1239
1241 | /tv/tv_writer/episodes_written	1240
1242 | /film/film/personal_appearances./film/personal_film_appearance/person	1241
1243 | /base/nobelprizes/nobel_prize_winner/nobel_honor./base/nobelprizes/nobel_honor/subject_area	1242
1244 | /organization/organization/phone_number./common/phone_number/service_location	1243
1245 | /sports/pro_athlete/teams./soccer/football_roster_position/team	1244
1246 | /time/event/locations	1245
1247 | /tv/tv_program/original_network./tv/tv_network_duration/network	1246
1248 | /ice_hockey/hockey_player/former_team_s./sports/sports_team_roster/team	1247
1249 | /broadcast/radio_format/stations	1248
1250 | /film/editor/film	1249
1251 | /travel/travel_destination/local_transportation	1250
1252 | /base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of	1251
1253 | /travel/travel_destination/how_to_get_here./travel/transportation/transport_terminus	1252
1254 | /dining/cuisine/restaurant	1253
1255 | /sports/sport_country/athletic_performances./sports/competitor_competition_relationship/competitors	1254
1256 | /location/statistical_region/rent50_4./measurement_unit/dated_money_value/currency	1255
1257 | /aviation/aircraft_model/aircraft_type	1256
1258 | /film/film_distribution_medium/films_distributed_in_this_medium./film/film_film_distributor_relationship/film	1257
1259 | /aviation/airline/alliance	1258
1260 | /organization/role/leaders./organization/leadership/organization	1259
1261 | /film/film/story_by	1260
1262 | /visual_art/art_subject/artwork_on_the_subject	1261
1263 | /comic_books/comic_book_character/regular_featured_appearances	1262
1264 | /award/award_ceremony/awards_presented./award/award_honor/award_winner	1263
1265 | /games/game_genre/boardgames	1264
1266 | /law/judge/courts./law/judicial_tenure/court	1265
1267 | /media_common/dedicated_work/dedication./media_common/dedication/dedicated_to	1266
1268 | /award/ranked_item/appears_in_ranked_lists./award/ranking/list	1267
1269 | /computer/programming_language/language_paradigms	1268
1270 | /sports/sports_team/roster./ice_hockey/hockey_previous_roster_position/player	1269
1271 | /military/military_person/participated_in_conflicts	1270
1272 | /geography/lake/lake_type	1271
1273 | /royalty/noble_title/noble_rank	1272
1274 | /government/legislative_session/members./government/government_position_held/district_represented	1273
1275 | /military/military_combatant/force_deployments./military/force_strength/combatant	1274
1276 | /film/film_subject/films	1275
1277 | /transportation/bridge/bridge_type	1276
1278 | /cvg/gameplay_mode/games_with_this_mode	1277
1279 | /base/popstra/celebrity/friendship./base/popstra/friendship/participant	1278
1280 | /symbols/namesake/named_after	1279
1281 | /user/mt/default_domain/metabolite/biofluid_location	1280
1282 | /basketball/basketball_position/player_roster_position./basketball/basketball_roster_position/player	1281
1283 | /base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location	1282
1284 | /cvg/computer_videogame/subjects	1283
1285 | /film/film/dubbing_performances./film/dubbing_performance/language	1284
1286 | /music/album/artist	1285
1287 | /celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity	1286
1288 | /cvg/computer_videogame/game_series	1287
1289 | /tv/tv_program/subjects	1288
1290 | /ice_hockey/hockey_position/players	1289
1291 | /base/thoroughbredracing/thoroughbred_racehorse/country	1290
1292 | /olympics/olympic_games/athletes./olympics/olympic_athlete_affiliation/country	1291
1293 | /business/business_operation/retained_earnings./measurement_unit/dated_money_value/currency	1292
1294 | /architecture/structure/architectural_style	1293
1295 | /food/ingredient/cuisine	1294
1296 | /film/film/personal_appearances./film/personal_film_appearance/type_of_appearance	1295
1297 | /award/award_nominated_work/award_nominations./award/award_nomination/award_nominee	1296
1298 | /tv/tv_location/tv_shows_filmed_here	1297
1299 | /location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency	1298
1300 | /sports/sports_position/players./basketball/basketball_roster_position/player	1299
1301 | /geography/island_group/islands_in_group	1300
1302 | /user/hangy/default_domain/sports_team_gender/gender	1301
1303 | /american_football/football_player/receiving./american_football/player_receiving_statistics/team	1302
1304 | /education/fraternity_sorority/founded_location	1303
1305 | /cvg/computer_videogame/developer	1304
1306 | /baseball/baseball_player/lifetime_batting_statistics./baseball/lifetime_batting_statistics/ending_season	1305
1307 | /base/newsevents/news_reporting_organisation/news_reports./base/newsevents/news_report/event	1306
1308 | /sports/pro_athlete/teams./sports/sports_team_roster/position	1307
1309 | /organization/organization/board_members./organization/organization_board_membership/member	1308
1310 | /royalty/royal_line/monarchs_from_this_line	1309
1311 | /base/petbreeds/dog_breed/color	1310
1312 | /film/film_character/portrayed_in_films_dubbed./film/dubbing_performance/language	1311
1313 | /sports/professional_sports_team/owner_s	1312
1314 | /fictional_universe/fictional_character/employers./fictional_universe/fictional_employment_tenure/title	1313
1315 | /broadcast/producer/produces	1314
1316 | /business/business_operation/liabilities./measurement_unit/dated_money_value/currency	1315
1317 | /organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency	1316
1318 | /award/award_category/category_of	1317
1319 | /architecture/museum/address./location/mailing_address/state_province_region	1318
1320 | /business/business_operation/current_assets./measurement_unit/dated_money_value/currency	1319
1321 | /royalty/chivalric_order_member/belongs_to_order./royalty/chivalric_order_membership/order	1320
1322 | /film/film/executive_produced_by	1321
1323 | /base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/convicted_person	1322
1324 | /royalty/monarch/kingdom	1323
1325 | /base/ecology/ecosystem/ecosystem_members	1324
1326 | /government/governmental_body/members./government/government_position_held/office_position_or_title	1325
1327 | /royalty/noble_title/used_in./royalty/system_title_relationship/system	1326
1328 | /location/country/official_language	1327
1329 | /music/performance_role/regular_performances./music/group_membership/member	1328
1330 | /user/radiusrs/default_domain/astrology/related_topics	1329
1331 | /sports/sports_team/roster./baseball/baseball_roster_position/position	1330
1332 | /people/place_of_interment/interred_here	1331
1333 | /sports/sports_championship_event/championship	1332
1334 | /sports/pro_athlete/teams./american_football/football_roster_position/position	1333
1335 | /film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium	1334
1336 | /military/rank/used_by_services./military/military_service/military_person	1335
1337 | /base/schemastaging/fish/found_in	1336
1338 | /user/robert/us_congress/congressional_district/state	1337
1339 | /sports/school_sports_team/school	1338
1340 | /location/country/administrative_divisions	1339
1341 | /award/award_winner/awards_won./award/award_honor/honored_for	1340
1342 | /people/appointee/position./people/appointment/appointed_role	1341
1343 | /olympics/olympic_medal/medal_winners./olympics/olympic_medal_honor/medalist	1342
1344 | /people/ethnicity/includes_groups	1343
1345 | /base/contractbridge/bridge_tournament/standing./base/contractbridge/bridge_tournament_standings/second_place	1344
1346 | 


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/data/WN18/relation2id.txt:
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 1 | _member_of_domain_topic	0
 2 | _member_meronym	1
 3 | _derivationally_related_form	2
 4 | _member_of_domain_region	3
 5 | _similar_to	4
 6 | _hypernym	5
 7 | _member_holonym	6
 8 | _instance_hypernym	7
 9 | _member_of_domain_usage	8
10 | _synset_domain_topic_of	9
11 | _hyponym	10
12 | _instance_hyponym	11
13 | _synset_domain_usage_of	12
14 | _has_part	13
15 | _verb_group	14
16 | _part_of	15
17 | _synset_domain_region_of	16
18 | _also_see	17
19 | 


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