├── README.md ├── all_relations.png ├── app.py ├── checkpoints ├── classifier │ └── model.bin └── entity_searcher │ └── search_tree.pkl ├── dialogue.png ├── neo_db ├── classifier.py ├── fin_config.py ├── graph_matcher.py ├── query_graph.py ├── robot_answer.py └── semantic_parser.py ├── query_node1.png ├── query_node2.png ├── requirement.txt ├── static ├── css │ ├── bootstrap.min.css │ ├── datatables.bootstrap.css │ ├── datatables.responsive.css │ ├── font-awesome.min.css │ ├── ionicons.min.css │ ├── main.css │ ├── nifty-demo-icons.min.css │ ├── nifty-demo.min.css │ ├── nifty.css │ ├── nifty.min.css │ ├── pace.min.css │ └── wiki.css ├── fonts │ ├── cjzkeoubrn4kerxqtauh3acwcynf_cdxxwclxiixg1c.ttf │ ├── fontawesome-webfont.ttf │ ├── fontawesome-webfont.woff │ ├── fontawesome-webfont.woff2 │ ├── mtp_ysujh_bn48vbg8snsonf5ufddttmlvmwujdhhgs.ttf │ ├── nifty-demo-icons.ttf │ └── nifty-demo-icons.woff ├── images │ ├── bg-mask.png │ ├── bg-reply-texture.jpg │ ├── bk3.jpg │ ├── btn-search-small.png │ ├── bubble-triangle.png │ ├── qa-banner.png │ ├── user-assistant.png │ └── 北邮LOGO.png └── js │ ├── bootstrap.min.js │ ├── echarts.min.js │ ├── icons.js │ ├── jquery-2.2.4.min.js │ ├── nifty-demo.min.js │ ├── nifty.min.js │ ├── pace.min.js │ └── tags.js ├── templates ├── all_relation.html ├── dialogue.html ├── index.html └── query_node.html ├── 封面.png ├── 程序流.png └── 项目结构.png /README.md: -------------------------------------------------------------------------------- 1 | # 基于金融知识图谱的知识计算引擎构建 2 | 3 | 前端代码参考[KGQA_HLM：基于知识图谱的《红楼梦》人物关系可视化及问答系统](https://github.com/chizhu/KGQA_HLM) 4 | 5 | 多轮对话机制参考[基于金融知识图谱的问答系统](https://github.com/XuekaiChen/FinKnowledgeGraph) 6 | 7 | ## 项目主图 8 | 9 | 封面 10 | 11 | ![封面](封面.png) 12 | 13 | 检索节点信息 14 | 15 | ![检索节点信息](query_node2.png) 16 | 17 | 图谱全貌 18 | 19 | ![图谱全貌](all_relations.png) 20 | 21 | 多轮对话 22 | 23 | ![多轮对话](dialogue.png) 24 | 25 | 程序流 26 | 27 | ![程序流](程序流.png) 28 | 29 | ## 准备数据 30 | 1. 利用结构化三元组构建金融知识图谱，数据可在[此处](https://pan.baidu.com/s/1UQfu5c1Y7BfdMS_uNGrZug )下载获得，提取码：`sae3` 31 | 2. 依照[此项目](https://github.com/XuekaiChen/FinKnowledgeGraph)中的中“2.安装环境”的提示，下载Neo4j 32 | * 创建Project：finance_demo 33 | * 在Project下创建数据库：db 34 | * 用户名：neo4j 35 | * 密码：neo4j123 36 | 3. 下载该项目中的`step2_store_to_neo4j.py`文件，运行生成后端金融知识图谱 37 | 38 | 39 | ## 运行 40 | 1. 开启Neo4j数据库finance_demo/db（默认7474端口） 41 | 2. 运行`app.py`主程序，点击生成的网址链接（默认5000端口） 42 | 43 | ### 项目结构如下： 44 | ![程序流](项目结构.png) 45 | -------------------------------------------------------------------------------- /all_relations.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/all_relations.png -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, render_template, request, jsonify, json 2 | from neo_db.query_graph import query, get_details, get_all_graph 3 | from neo_db.robot_answer import get_robot_answer 4 | 5 | app = Flask(__name__) 6 | 7 | 8 | @app.route('/', methods=['GET', 'POST']) 9 | @app.route('/index', methods=['GET', 'POST']) 10 | def index(name=None): 11 | return render_template('index.html', name=name) 12 | 13 | 14 | @app.route('/query_node', methods=['GET', 'POST']) 15 | def search_page(): 16 | return render_template('query_node.html') 17 | 18 | 19 | @app.route('/get_all_relation', methods=['GET', 'POST']) 20 | def get_all_relation(): 21 | return render_template('all_relation.html') 22 | 23 | 24 | @app.route('/dialogue', methods=['GET', 'POST']) 25 | def dialogue_page(): 26 | # 需要返回json数据格式嵌入HTML 27 | return render_template('dialogue.html') 28 | 29 | 30 | @app.route('/get_profile',methods=['GET','POST']) 31 | def get_profile(): 32 | choice = request.args.get('choice') 33 | limit = request.args.get('limit') 34 | json_data = get_all_graph(choice, limit) 35 | return jsonify(json_data) 36 | 37 | 38 | @app.route('/search_node', methods=['GET', 'POST']) 39 | def search_node(): 40 | choice = request.args.get('choice') 41 | name = request.args.get('name') 42 | json_data = query(choice, str(name)) 43 | return jsonify(json_data) 44 | 45 | 46 | @app.route('/get_chart', methods=['GET', 'POST']) 47 | def get_chart(): 48 | node_or_edge = json.loads(request.form.get('type')) # $.ajax传多个参数只能post请求，对应form 49 | data = json.loads(request.form.get('data')) 50 | nodes = json.loads(request.form.get('nodes')) 51 | json_data = get_details(node_or_edge, data, nodes) 52 | return jsonify(json_data) 53 | 54 | 55 | @app.route('/dialogue_answer', methods=['GET', 'POST']) 56 | def dialogue_answer(): 57 | question = request.args.get('name') 58 | robot_answer = get_robot_answer(str(question).strip()) 59 | json_data = {'data': robot_answer} 60 | json_data['data'].replace("\n", "
") 61 | return jsonify(json_data) 62 | 63 | 64 | if __name__ == '__main__': 65 | app.debug = True 66 | app.run() 67 | -------------------------------------------------------------------------------- /checkpoints/classifier/model.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/checkpoints/classifier/model.bin -------------------------------------------------------------------------------- /checkpoints/entity_searcher/search_tree.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/checkpoints/entity_searcher/search_tree.pkl -------------------------------------------------------------------------------- /dialogue.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/dialogue.png -------------------------------------------------------------------------------- /neo_db/classifier.py: -------------------------------------------------------------------------------- 1 | import fasttext 2 | import jieba 3 | # from KGQA_HLM.config import classifier_corpus_path, classifier_save_path 4 | 5 | 6 | def train_classifier(input_file_path, model_save_path): 7 | """训练分类模型""" 8 | 9 | # 基于 fasttext api 实现模型训练 10 | # https://fasttext.cc/docs/en/supervised-tutorial.html 11 | model = fasttext.train_supervised(input=input_file_path, label='__label__',lr=0.5) 12 | result = model.test(input_file_path) 13 | print(result[1]) 14 | print(result[2]) 15 | model.save_model(model_save_path) 16 | 17 | 18 | class Classifier: 19 | """分类器""" 20 | 21 | def __init__(self, model_load_path): 22 | self.model_load_path = model_load_path 23 | self.model = self.load_model() 24 | 25 | def load_model(self): 26 | """加载模型""" 27 | return fasttext.load_model(self.model_load_path) 28 | 29 | def predict(self, query): 30 | """预测 query""" 31 | 32 | # 基于 fasttext api 实现模型预测 33 | # https://fasttext.cc/docs/en/supervised-tutorial.html 34 | query_intent = self.model.predict(query) 35 | # 预测 label 和概率 36 | return query_intent[0][0].replace('__label__', ''), query_intent[1][0] 37 | 38 | 39 | if __name__ == '__main__': 40 | 41 | print('开始训练分类器...') 42 | 43 | train_classifier(classifier_corpus_path, classifier_save_path) 44 | 45 | print('分类器训练成功...') 46 | 47 | 48 | -------------------------------------------------------------------------------- /neo_db/fin_config.py: -------------------------------------------------------------------------------- 1 | from py2neo import Graph 2 | 3 | # 加载知识图谱 4 | graph = Graph("http://localhost:7474", auth=("neo4j", "neo4j123")) 5 | 6 | # 知识语料路径 7 | entity_corpus_path = 'data/knowledge/' 8 | 9 | # 实体搜索器存储路径 10 | entity_searcher_save_path = 'checkpoints/entity_searcher/search_tree.pkl' 11 | 12 | # 实体搜索器加载路径 13 | entity_searcher_load_path = 'checkpoints/entity_searcher/search_tree.pkl' 14 | 15 | # 分类器语料路径 16 | classifier_corpus_path = 'data/classifier/chat.train' 17 | 18 | # 分类器模型存储路径 19 | classifier_save_path = 'checkpoints/classifier/model.bin' 20 | 21 | # 分类器模型加载路径 22 | classifier_load_path = 'checkpoints/classifier/model.bin' 23 | 24 | # 闲聊回复语料库 25 | chat_responses = { 26 | 'qa': [], 27 | 'greet': [ 28 | 'hello，很高兴为您服务，有什么可以为您效劳的呢？', 29 | '您好，可以输入股票名称或者代码查看详细信息哦', 30 | '您好，有股票相关的问题可以问我哦' 31 | ], 32 | 'goodbye': [ 33 | '再见', 34 | '有什么问题可以下次继续问我哦', 35 | '拜拜喽，别忘了给个小红心啊', 36 | ], 37 | 'bot': [ 38 | '没错，我就是集美貌与才智于一身的智能问答机器人', 39 | '为了防止世界被破坏，为了维护世界的和平，有任何需要我都会帮助你的' 40 | ], 41 | 'safe': [ 42 | '不好意思，您的问题我没太听懂，可以换一种说法嘛', 43 | '亲亲，这里好像没有您想要的答案' 44 | ] 45 | } 46 | 47 | # 问题类型 48 | question_types = { 49 | 'concept': 50 | ['概念', '特征'], 51 | 'holder': 52 | ['股东'], 53 | 'stock': 54 | ['股票', '持有', '控股', '控制'], 55 | 'industry': 56 | ['行业', '领域'], 57 | } 58 | 59 | # 存储对话历史中上一次涉及的问题类型和实体 60 | contexts = { 61 | 'ques_types': None, 62 | 'entities': None 63 | } 64 | 65 | # 节点Legend列表 66 | CA_LIST = {"股东": 0, "股票": 1, "概念": 2} 67 | 68 | d={ 69 | "segments": [ 70 | { 71 | "start": { 72 | "identity": 0, 73 | "labels": [ 74 | "股票" 75 | ], 76 | "properties": { 77 | "股票名称": "泛海控股", 78 | "TS代码": "000046.SZ", 79 | "行业": "多元金融", 80 | "股票代码": 46 81 | } 82 | }, 83 | "relationship": { 84 | "identity": 9601, 85 | "start": 0, 86 | "end": 4301, 87 | "type": "所属概念", 88 | "properties": { 89 | 90 | } 91 | }, 92 | "end": { 93 | "identity": 4301, 94 | "labels": [ 95 | "概念" 96 | ], 97 | "properties": { 98 | "概念代码": "TS199", 99 | "概念名称": "房地产" 100 | } 101 | } 102 | } 103 | ], 104 | "length": 1.0 105 | } 106 | -------------------------------------------------------------------------------- /neo_db/graph_matcher.py: -------------------------------------------------------------------------------- 1 | from py2neo import Graph 2 | 3 | 4 | class GraphMatcher: 5 | """基于 cypher 语句查询数据库""" 6 | 7 | def __init__(self): 8 | self.graph = Graph('http://localhost:7474/finance_demo/db/', auth=('neo4j', 'neo4j123')) 9 | 10 | def parse_graph(self, ques_types, entities): 11 | """转换成 cypher 语句查询""" 12 | 13 | response = "" 14 | for each_ques_type in ques_types: 15 | # 问所属概念 16 | if each_ques_type == 'concept': 17 | for entity_name, entity_type in entities.items(): 18 | # 1、问股票的概念 19 | if entity_type == '股票': 20 | cypher_sql = f'MATCH (s:`股票`)-[r:所属概念]->(c:`概念`) where s.股票名称 = "{entity_name}" return c.概念名称' 21 | rtn = self.graph.run(cypher_sql).data() 22 | response += f'{entity_name}所属概念有{"、".join([i["c.概念名称"] for i in rtn])}
' 23 | # 2、问概念有哪些股票 24 | elif entity_type == '概念': 25 | cypher_sql = f'MATCH (s:`股票`)-[r:所属概念]->(c:`概念`) where c.概念名称 = "{entity_name}" return s.股票名称' 26 | rtn = self.graph.run(cypher_sql).data() 27 | response += f'{entity_name}概念下有{"、".join([i["s.股票名称"] for i in rtn])}这些股票
' 28 | 29 | # 问股东的股票 30 | elif each_ques_type == 'stock': 31 | for entity_name, entity_type in entities.items(): 32 | cypher_sql = f'MATCH (s:`股东`)-[r:持有]->(c:`股票`) where s.股东名称 = "{entity_name}" return c.股票名称, r.持有量, r.占比' 33 | rtn = self.graph.run(cypher_sql).data() 34 | response = f'{entity_name}持有股票的情况如下:\n' 35 | for i in rtn: 36 | response += f'{i["c.股票名称"]}，持有股份{i["r.持有量"]}，占比{i["r.占比"]}%
' 37 | 38 | # 问股票的股东 39 | elif each_ques_type == 'holder': 40 | for entity_name, entity_type in entities.items(): 41 | cypher_sql = f'MATCH (s:`股东`)-[r:持有]->(c:`股票`) where c.股票名称 = "{entity_name}" return s.股东名称, r.持有量, r.占比' 42 | rtn = self.graph.run(cypher_sql).data() 43 | response = f'{entity_name}的股东是:\n' 44 | for i in rtn: 45 | response += f'{i["s.股东名称"]}，持有股份{i["r.持有量"]}，占比{i["r.占比"]}%
' 46 | 47 | # 问行业、领域 48 | elif each_ques_type == 'industry': 49 | # 提示：match 股票 return 行业 50 | for entity_name, entity_type in entities.items(): 51 | # 1、股票所属行业 52 | if entity_type == '股票': 53 | cypher_sql = f'MATCH (s:`股票`) where s.股票名称="{entity_name}" return s.行业' 54 | rtn = self.graph.run(cypher_sql).data() 55 | response += f'{entity_name}所属行业是{rtn[0]["s.行业"]}
' 56 | pass 57 | return response.strip() 58 | 59 | def predict(self, semantics): 60 | """预测 query""" 61 | response = self.parse_graph(semantics['ques_types'], semantics['entities']) 62 | return response 63 | -------------------------------------------------------------------------------- /neo_db/query_graph.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Date : 2022/5/19 3 | # Author : Chen Xuekai 4 | # Description : 5 | 6 | import sys 7 | import json 8 | from neo_db.fin_config import graph 9 | from pprint import pprint 10 | 11 | 12 | # 因为数据格式实在不固定，只能每类单独写了 13 | def query(node_cate, q_content): 14 | json_data = {'nodes': [], "links": []} 15 | 16 | # 总布局：股东-[持有]->股票-[所属概念]->概念 17 | if node_cate == '0': # 股东 18 | data = graph.run("match(p:`股东`{`股东名称`:'%s'})-[r]->(n) return n" % q_content) 19 | data = list(data) 20 | 21 | if len(data) == 0: 22 | pass 23 | else: 24 | # 添加查询节点 25 | node = { 26 | 'id': 0, 27 | 'name': q_content, 28 | 'category': 0 # 股东 29 | } 30 | json_data['nodes'].append(node) 31 | # # 添加受体节点 32 | for idx, n in enumerate(data): 33 | node = { 34 | 'id': idx + 1, 35 | 'name': n['n']['股票名称'], 36 | 'category': 1 # 股票 37 | } 38 | json_data['nodes'].append(node) 39 | # 添加关系 40 | for idx in range(len(data)): 41 | link = { 42 | 'source': 0, 43 | 'target': idx + 1, 44 | 'value': "持有" 45 | } 46 | json_data['links'].append(link) 47 | 48 | elif node_cate == '1': # 股票 49 | data1 = list(graph.run("match(p)-[r]->(n:`股票`{`股票名称`:'%s'}) return p" % q_content)) 50 | if len(data1) == 0: 51 | pass 52 | else: 53 | # 添加查询节点 54 | node = { 55 | 'id': 0, 56 | 'name': q_content, 57 | 'category': 1 # 股票 58 | } 59 | json_data['nodes'].append(node) 60 | # 添加节点 61 | for idx, n in enumerate(data1): 62 | node = { 63 | 'id': idx + 1, 64 | 'name': n['p']['股东名称'], 65 | 'category': 0 66 | } 67 | json_data['nodes'].append(node) 68 | # 添加关系 69 | for idx in range(len(data1)): 70 | link = { 71 | 'source': idx + 1, 72 | 'target': 0, 73 | 'value': "持有" 74 | } 75 | json_data['links'].append(link) 76 | 77 | data2 = list(graph.run("match(p:`股票`{`股票名称`:'%s'})-[r]->(n) return n" % q_content)) 78 | if len(data2) == 0: 79 | pass 80 | else: 81 | if len(data1) == 0: 82 | node = { 83 | 'id': 0, 84 | 'name': q_content, 85 | 'category': 1 # 股票 86 | } 87 | json_data['nodes'].append(node) 88 | else: 89 | existing = len(json_data['nodes']) 90 | # 添加受体节点 91 | for idx, n in enumerate(data2): 92 | node = { 93 | 'id': existing + idx, 94 | 'name': n['n']['概念名称'], 95 | 'category': 2 96 | } 97 | json_data['nodes'].append(node) 98 | # 添加关系 99 | for idx in range(len(data2)): 100 | link = { 101 | 'source': 0, 102 | 'target': existing + idx, 103 | 'value': "所属概念" 104 | } 105 | json_data['links'].append(link) 106 | 107 | elif node_cate == '2': # 概念 108 | data = graph.run("match(p)-[r]->(n:`概念`{`概念名称`:'%s'}) return p" % q_content) 109 | data = list(data) 110 | 111 | if len(data) == 0: 112 | pass 113 | else: 114 | # 添加查询节点 115 | node = { 116 | 'id': 0, 117 | 'name': q_content, 118 | 'category': 2 # 概念 119 | } 120 | json_data['nodes'].append(node) 121 | # 添加节点 122 | for idx, n in enumerate(data): 123 | node = { 124 | 'id': idx + 1, 125 | 'name': n['p']['股票名称'], 126 | 'category': 1 # 股票 127 | } 128 | json_data['nodes'].append(node) 129 | # 添加关系 130 | for idx in range(len(data)): 131 | link = { 132 | 'source': idx + 1, 133 | 'target': 0, 134 | 'value': "所属概念" 135 | } 136 | json_data['links'].append(link) 137 | 138 | else: 139 | sys.exit() 140 | 141 | return json_data # 返回列表[json_data{data:[],links:[]}, detail_chart{key:value}] 142 | 143 | 144 | # 点击echarts元素时获取信息表 145 | def get_details(node_or_edge, data, nodes): # json类型的p/r/n 146 | """ 147 | node_or_edge: node 148 | data: {'category': 0, 'id': 0, 'name': '王石'} 149 | nodes: [{'category': 0, 'id': 0, 'name': '王石'}, {'category': 1, 'id': 1, 'name': '宝莱特'}] 150 | 151 | edge 152 | {'source': 0, 'target': 1, 'value': '持有'} 153 | [{'category': 0, 'id': 0, 'name': '王石'}, {'category': 1, 'id': 1, 'name': '宝莱特'}] 154 | """ 155 | result = {} 156 | if node_or_edge == 'node': 157 | if data['category'] == 0: # 股东 158 | result = {'id': data['id'], '股东名称': data['name']} 159 | elif data['category'] == 1: # 股票 160 | result['id'] = data['id'] 161 | response = list(graph.run("MATCH (n:`股票`{`股票名称`:'%s'}) RETURN n" % data['name'])) 162 | for i in response[0]['n']: 163 | result[i] = str(response[0]['n'][i]) 164 | 165 | elif data['category'] == 2: # 概念 166 | result['id'] = data['id'] 167 | response = list(graph.run("MATCH (n:`概念`{`概念名称`:'%s'}) RETURN n" % data['name'])) 168 | for i in response[0]['n']: 169 | result[i] = str(response[0]['n'][i]) 170 | 171 | elif node_or_edge == 'edge': 172 | if data['value'] == '持有': 173 | result['关系类型'] = '持有' 174 | source = [item['name'] for item in nodes if item['id']==data['source']][0] 175 | target = [item['name'] for item in nodes if item['id']==data['target']][0] 176 | response = list( 177 | graph.run("MATCH l=(p:`股东`{`股东名称`:'%s'})-[r:`持有`]->(n:`股票`{`股票名称`:'%s'}) RETURN r" % (source, target)) 178 | ) 179 | for i in response[0]['r']: 180 | result[i] = response[0]['r'][i] 181 | if i == '占比': 182 | result[i] = str(response[0]['r'][i]) + '%' 183 | 184 | elif data['value'] == '所属概念': 185 | result = {'关系类型': '所属概念'} 186 | 187 | else: 188 | sys.exit() 189 | 190 | chart = dict_to_html(result) 191 | return chart 192 | 193 | 194 | # 将get_details得到的dict转换为html格式 195 | def dict_to_html(info_dict): # dict{key: value} 196 | s = '' 197 | for key, value in info_dict.items(): 198 | st = "
" + str(key) + " \ 199 |
" + str(value) + "
" 200 | s += st 201 | return s 202 | 203 | 204 | # 205 | def get_all_graph(choice, limit): 206 | json_data = {'nodes': [], "links": []} 207 | 208 | if choice == '0': # 持有 209 | response = graph.run("MATCH (p)-[r:`持有`]->(n) RETURN p,id(p),labels(p),r,n,id(n),labels(n) LIMIT %d" % int(limit)).data() # list 210 | exist_name = [] 211 | for line in response: 212 | if line['p']['股东名称'] not in exist_name: 213 | source = { 214 | 'id': len(exist_name), 215 | 'name': line['p']['股东名称'], 216 | 'category': 0 217 | } 218 | exist_name.append(line['p']['股东名称']) 219 | json_data['nodes'].append(source) 220 | else: pass 221 | if line['n']['股票名称'] not in exist_name: 222 | target = { 223 | 'id': len(exist_name), 224 | 'name': line['n']['股票名称'], 225 | 'category': 1 226 | } 227 | exist_name.append(line['n']['股票名称']) 228 | json_data['nodes'].append(target) 229 | else: pass 230 | link = { 231 | 'source': exist_name.index(line['p']['股东名称']), 232 | 'target': exist_name.index(line['n']['股票名称']), 233 | 'value': '持有' 234 | } 235 | json_data['links'].append(link) 236 | 237 | elif choice == '1': # 所属概念 238 | response = graph.run("MATCH (p)-[r:`所属概念`]->(n) RETURN p,id(p),labels(p),r,n,id(n),labels(n) LIMIT %d" % int(limit)).data() # list 239 | exist_name = [] 240 | for line in response: 241 | if line['p']['股票名称'] not in exist_name: 242 | source = { 243 | 'id': len(exist_name), 244 | 'name': line['p']['股票名称'], 245 | 'category': 1 246 | } 247 | exist_name.append(line['p']['股票名称']) 248 | json_data['nodes'].append(source) 249 | else: 250 | pass 251 | if line['n']['概念名称'] not in exist_name: 252 | target = { 253 | 'id': len(exist_name), 254 | 'name': line['n']['概念名称'], 255 | 'category': 2 256 | } 257 | exist_name.append(line['n']['概念名称']) 258 | json_data['nodes'].append(target) 259 | else: 260 | pass 261 | link = { 262 | 'source': exist_name.index(line['p']['股票名称']), 263 | 'target': exist_name.index(line['n']['概念名称']), 264 | 'value': '所属概念' 265 | } 266 | json_data['links'].append(link) 267 | 268 | return json_data 269 | 270 | 271 | if __name__ == "__main__": 272 | # js_data0 = query(0, "中信证券") 273 | # pprint(js_data0) 274 | # print("-" * 100) 275 | # js_data1 = query(1, "宁德时代") 276 | # pprint(js_data1) 277 | # print("-"*100) 278 | # js_data2 = query(2, "券商") 279 | # pprint(js_data2) 280 | 281 | # node_or_edge = 'node' 282 | # data = {'category': 0, 'id': 0, 'name': '王石'} 283 | # nodes = [{'category': 0, 'id': 0, 'name': '王石'}, {'category': 1, 'id': 1, 'name': '宝莱特'}] 284 | # node_or_edge = 'edge' 285 | # data = {'source': 0, 'target': 1, 'value': '持有'} 286 | # nodes = [{'category': 0, 'id': 0, 'name': '王石'}, {'category': 1, 'id': 1, 'name': '宝莱特'}] 287 | # get_details(node_or_edge, data, nodes) 288 | 289 | js_data = get_all_graph('0', '25') 290 | pprint(js_data) 291 | 292 | -------------------------------------------------------------------------------- /neo_db/robot_answer.py: -------------------------------------------------------------------------------- 1 | from neo_db.classifier import Classifier 2 | from neo_db.semantic_parser import SemanticParser 3 | from neo_db.graph_matcher import GraphMatcher 4 | from neo_db.fin_config import classifier_load_path, entity_searcher_load_path, chat_responses, question_types 5 | from random import choice 6 | 7 | # 加载闲聊分类器 8 | classifier = Classifier(classifier_load_path) 9 | 10 | # 加载语义解析器，预测问题类型和涉及的实体 11 | semantic_parser = SemanticParser(entity_searcher_load_path, question_types) 12 | 13 | # 加载图数据库查询 14 | graph_matcher = GraphMatcher() 15 | 16 | 17 | def get_robot_answer(query): 18 | # 预测 label 和概率 19 | query_intent_label, query_intent_prob = classifier.predict(query) 20 | response = "" 21 | # 知识问答 22 | semantics = semantic_parser.predict(query) 23 | if len(semantics['ques_types']) > 0 and len(semantics['entities']) > 0: 24 | response = graph_matcher.predict(semantics) 25 | # 闲聊 26 | elif query_intent_prob > 0.8: 27 | response = choice(chat_responses[query_intent_label]) 28 | if response == "": 29 | response = choice(chat_responses['safe']) 30 | 31 | return response 32 | 33 | 34 | if __name__ == '__main__': 35 | print(get_robot_answer('中信证券持有的股票有哪些？')) 36 | print("-"*100) 37 | print(get_robot_answer('宁德时代的概念是什么？')) 38 | 39 | -------------------------------------------------------------------------------- /neo_db/semantic_parser.py: -------------------------------------------------------------------------------- 1 | from neo_db.fin_config import entity_searcher_load_path, question_types, contexts, entity_corpus_path, entity_searcher_save_path 2 | import ahocorasick 3 | import pandas as pd 4 | import os 5 | import pickle 6 | from tqdm import tqdm 7 | 8 | 9 | def build_search_tree(input_folder_path, tree_save_path): 10 | """读取股票名称，股东和概念实体，构建 ac 树""" 11 | # https://pypi.org/project/pyahocorasick/ 12 | tree = ahocorasick.Automaton() 13 | 14 | stock_basic = pd.read_csv(os.path.join(input_folder_path, '股票信息.csv'), encoding='gbk') 15 | # 遍历 stock_basic，添加 name 即股票名字 16 | # 股票名字为key，value表示为具体的实体类型，比如：tree.add_word('股票名A', ('股票名A', '股票')) 17 | for idx, each_row in tqdm(stock_basic.iterrows()): 18 | tree.add_word(str(each_row['name']), (str(each_row['name']), '股票')) 19 | 20 | concept = pd.read_csv(os.path.join(input_folder_path, '概念信息.csv'), encoding='gbk') 21 | # 遍历 concept，添加 name 即概念名字 22 | # 概念名字为key，value表示为具体的实体类型，比如：tree.add_word('概念名A', ('概念名A', '概念')) 23 | for idx, each_row in tqdm(concept.iterrows()): 24 | tree.add_word(str(each_row['name']), (str(each_row['name']), '概念')) 25 | 26 | holder = pd.read_csv(os.path.join(input_folder_path, '股东信息.csv'), encoding='gbk') 27 | # 遍历 holder，添加 股东名称 28 | # 股东名称为key，value表示为具体的实体类型，比如：tree.add_word('股东名称A', ('股东名称A', '股东')) 29 | for idx, each_row in tqdm(holder.iterrows()): 30 | tree.add_word(str(each_row['name']), (str(each_row['name']), '股东')) 31 | 32 | tree.make_automaton() 33 | 34 | with open(tree_save_path, 'wb') as fout: 35 | pickle.dump(tree, fout) 36 | 37 | 38 | class SemanticParser: 39 | """实体搜索器""" 40 | def __init__(self, entity_model_load_path, question_types): 41 | self.entity_model_load_path = entity_model_load_path 42 | self.entity_model = self.load_model() 43 | self.question_types = question_types 44 | 45 | def load_model(self): 46 | """加载模型""" 47 | with open(self.entity_model_load_path, 'rb') as fin: 48 | return pickle.load(fin) 49 | 50 | def predict_question_types(self, query): 51 | """判断问题类型，这里只是通过关键词去判断，可以改成分类模型""" 52 | rtn_ques_types = [] 53 | for ques_type, kws in self.question_types.items(): 54 | for each_kw in kws: 55 | if each_kw in query: 56 | rtn_ques_types.append(ques_type) 57 | break 58 | return rtn_ques_types 59 | 60 | def predict(self, query): 61 | """预测 query""" 62 | rtn = {} 63 | # 预测类型 64 | ques_types = self.predict_question_types(query) 65 | 66 | # 预测实体 67 | entities = {} 68 | for end_index, (entity_name, entity_type) in self.entity_model.iter(query): 69 | entities[entity_name] = entity_type 70 | 71 | # 问句中至少有一种实体 72 | if len(ques_types) != 0 and len(entities) != 0: 73 | rtn['ques_types'] = ques_types 74 | rtn['entities'] = entities 75 | # 备份上下文 76 | contexts['ques_types'] = ques_types 77 | contexts['entities'] = entities 78 | 79 | # 问句中有问题类型没实体，需要结合上下文问题 80 | elif len(ques_types) != 0: 81 | rtn['ques_types'] = ques_types 82 | # 备份 83 | contexts['ques_types'] = ques_types 84 | # 从对话历史中继承问题类型 85 | rtn['entities'] = contexts['entities'] 86 | 87 | # 问句中有实体没问句类型，结合上下文实体 88 | elif len(entities) != 0: 89 | # 从对话历史中继承问题类型 90 | rtn['ques_types'] = contexts['ques_types'] 91 | rtn['entities'] = entities 92 | # 备份 93 | contexts['entities'] = entities 94 | else: 95 | # 如果两个都没有找到，说明是没有涉及 KG 96 | rtn['ques_types'] = [] 97 | rtn['entities'] = {} 98 | 99 | return rtn 100 | 101 | 102 | if __name__ == '__main__': 103 | # print('开始训练实体搜索树...') 104 | # build_search_tree(entity_corpus_path, entity_searcher_save_path) 105 | # print('实体搜索树训练成功...') 106 | 107 | semantic_parser = SemanticParser(entity_searcher_load_path, question_types) 108 | semantics = semantic_parser.predict("宁德时代的概念是什么？") 109 | print(semantics) 110 | 111 | -------------------------------------------------------------------------------- /query_node1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/query_node1.png -------------------------------------------------------------------------------- /query_node2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XuekaiChen/ShowKnowledge/e12284f81bb00db2e1066ad9b74b5f0a1bc11655/query_node2.png -------------------------------------------------------------------------------- /requirement.txt: -------------------------------------------------------------------------------- 1 | flask 2 | py2neo 3 | pyltp 4 | bs4 5 | -------------------------------------------------------------------------------- /static/css/datatables.bootstrap.css: -------------------------------------------------------------------------------- 1 | div.dataTables_length label { 2 | font-weight: normal; 3 | text-align: left; 4 | white-space: nowrap; 5 | } 6 | 7 | div.dataTables_length select { 8 | width: 75px; 9 | display: inline-block; 10 | } 11 | 12 | div.dataTables_filter { 13 | text-align: right; 14 | } 15 | 16 | div.dataTables_filter label { 17 | font-weight: normal; 18 | white-space: nowrap; 19 | text-align: left; 20 | } 21 | 22 | div.dataTables_filter input { 23 | margin-left: 0.5em; 24 | display: inline-block; 25 | width:auto 26 | } 27 | 28 | div.dataTables_info { 29 | padding-top: 8px; 30 | white-space: nowrap; 31 | } 32 | 33 | div.dataTables_paginate { 34 | margin: 0; 35 | white-space: nowrap; 36 | text-align: right; 37 | } 38 | 39 | div.dataTables_paginate ul.pagination { 40 | margin: 2px 0; 41 | white-space: nowrap; 42 | } 43 | 44 | @media screen and (max-width: 767px) { 45 | div.dataTables_length, 46 | div.dataTables_filter, 47 | div.dataTables_info, 48 | div.dataTables_paginate { 49 | text-align: center; 50 | } 51 | } 52 | 53 | 54 | table.dataTable td, 55 | table.dataTable th { 56 | -webkit-box-sizing: content-box; 57 | -moz-box-sizing: content-box; 58 | box-sizing: content-box; 59 | } 60 | 61 | 62 | table.dataTable { 63 | clear: both; 64 | margin-top: 6px !important; 65 | margin-bottom: 6px !important; 66 | max-width: none !important; 67 | } 68 | 69 | table.dataTable thead .sorting, 70 | table.dataTable thead .sorting_asc, 71 | table.dataTable thead .sorting_desc, 72 | table.dataTable thead .sorting_asc_disabled, 73 | table.dataTable thead .sorting_desc_disabled { 74 | cursor: pointer; 75 | } 76 | 77 | table.dataTable thead .sorting { background: url('../images/sort_both.png') no-repeat center right; } 78 | table.dataTable thead .sorting_asc { background: url('../images/sort_asc.png') no-repeat center right; } 79 | table.dataTable thead .sorting_desc { background: url('../images/sort_desc.png') no-repeat center right; } 80 | 81 | table.dataTable thead .sorting_asc_disabled { background: url('../images/sort_asc_disabled.png') no-repeat center right; } 82 | table.dataTable thead .sorting_desc_disabled { background: url('../images/sort_desc_disabled.png') no-repeat center right; } 83 | 84 | table.dataTable thead > tr > th { 85 | padding-left: 18px; 86 | padding-right: 18px; 87 | } 88 | 89 | table.dataTable th:active { 90 | outline: none; 91 | } 92 | 93 | /* Scrolling */ 94 | div.dataTables_scrollHead table { 95 | margin-bottom: 0 !important; 96 | border-bottom-left-radius: 0; 97 | border-bottom-right-radius: 0; 98 | } 99 | 100 | div.dataTables_scrollHead table thead tr:last-child th:first-child, 101 | div.dataTables_scrollHead table thead tr:last-child td:first-child { 102 | border-bottom-left-radius: 0 !important; 103 | border-bottom-right-radius: 0 !important; 104 | } 105 | 106 | div.dataTables_scrollBody table { 107 | border-top: none; 108 | margin-top: 0 !important; 109 | margin-bottom: 0 !important; 110 | } 111 | 112 | div.dataTables_scrollBody tbody tr:first-child th, 113 | div.dataTables_scrollBody tbody tr:first-child td { 114 | border-top: none; 115 | } 116 | 117 | div.dataTables_scrollFoot table { 118 | margin-top: 0 !important; 119 | border-top: none; 120 | } 121 | 122 | /* Frustratingly the border-collapse:collapse used by Bootstrap makes the column 123 | width calculations when using scrolling impossible to align columns. We have 124 | to use separate 125 | */ 126 | table.table-bordered.dataTable { 127 | border-collapse: separate !important; 128 | } 129 | table.table-bordered thead th, 130 | table.table-bordered thead td { 131 | border-left-width: 0; 132 | border-top-width: 0; 133 | } 134 | table.table-bordered tbody th, 135 | table.table-bordered tbody td { 136 | border-left-width: 0; 137 | border-bottom-width: 0; 138 | } 139 | table.table-bordered th:last-child, 140 | table.table-bordered td:last-child { 141 | border-right-width: 0; 142 | } 143 | div.dataTables_scrollHead table.table-bordered { 144 | border-bottom-width: 0; 145 | } 146 | 147 | 148 | 149 | 150 | /* 151 | * TableTools styles 152 | */ 153 | .table.dataTable tbody tr.active td, 154 | .table.dataTable tbody tr.active th { 155 | background-color: #08C; 156 | color: white; 157 | } 158 | 159 | .table.dataTable tbody tr.active:hover td, 160 | .table.dataTable tbody tr.active:hover th { 161 | background-color: #0075b0 !important; 162 | } 163 | 164 | .table.dataTable tbody tr.active th > a, 165 | .table.dataTable tbody tr.active td > a { 166 | color: white; 167 | } 168 | 169 | .table-striped.dataTable tbody tr.active:nth-child(odd) td, 170 | .table-striped.dataTable tbody tr.active:nth-child(odd) th { 171 | background-color: #017ebc; 172 | } 173 | 174 | table.DTTT_selectable tbody tr { 175 | cursor: pointer; 176 | } 177 | 178 | div.DTTT .btn:hover { 179 | text-decoration: none !important; 180 | } 181 | 182 | ul.DTTT_dropdown.dropdown-menu { 183 | z-index: 2003; 184 | } 185 | 186 | ul.DTTT_dropdown.dropdown-menu a { 187 | color: #333 !important; /* needed only when demo_page.css is included */ 188 | } 189 | 190 | ul.DTTT_dropdown.dropdown-menu li { 191 | position: relative; 192 | } 193 | 194 | ul.DTTT_dropdown.dropdown-menu li:hover a { 195 | background-color: #0088cc; 196 | color: white !important; 197 | } 198 | 199 | div.DTTT_collection_background { 200 | z-index: 2002; 201 | } 202 | 203 | /* TableTools information display */ 204 | div.DTTT_print_info { 205 | position: fixed; 206 | top: 50%; 207 | left: 50%; 208 | width: 400px; 209 | height: 150px; 210 | margin-left: -200px; 211 | margin-top: -75px; 212 | text-align: center; 213 | color: #333; 214 | padding: 10px 30px; 215 | opacity: 0.95; 216 | 217 | background-color: white; 218 | border: 1px solid rgba(0, 0, 0, 0.2); 219 | border-radius: 6px; 220 | 221 | -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.5); 222 | box-shadow: 0 3px 7px rgba(0, 0, 0, 0.5); 223 | } 224 | 225 | div.DTTT_print_info h6 { 226 | font-weight: normal; 227 | font-size: 28px; 228 | line-height: 28px; 229 | margin: 1em; 230 | } 231 | 232 | div.DTTT_print_info p { 233 | font-size: 14px; 234 | line-height: 20px; 235 | } 236 | 237 | div.dataTables_processing { 238 | position: absolute; 239 | top: 50%; 240 | left: 50%; 241 | width: 100%; 242 | height: 60px; 243 | margin-left: -50%; 244 | margin-top: -25px; 245 | padding-top: 20px; 246 | padding-bottom: 20px; 247 | text-align: center; 248 | font-size: 1.2em; 249 | background-color: white; 250 | background: -webkit-gradient(linear, left top, right top, color-stop(0%, rgba(255,255,255,0)), color-stop(25%, rgba(255,255,255,0.9)), color-stop(75%, rgba(255,255,255,0.9)), color-stop(100%, rgba(255,255,255,0))); 251 | background: -webkit-linear-gradient(left, rgba(255,255,255,0) 0%, rgba(255,255,255,0.9) 25%, rgba(255,255,255,0.9) 75%, rgba(255,255,255,0) 100%); 252 | background: -moz-linear-gradient(left, rgba(255,255,255,0) 0%, rgba(255,255,255,0.9) 25%, rgba(255,255,255,0.9) 75%, rgba(255,255,255,0) 100%); 253 | background: -ms-linear-gradient(left, rgba(255,255,255,0) 0%, rgba(255,255,255,0.9) 25%, rgba(255,255,255,0.9) 75%, rgba(255,255,255,0) 100%); 254 | background: -o-linear-gradient(left, rgba(255,255,255,0) 0%, rgba(255,255,255,0.9) 25%, rgba(255,255,255,0.9) 75%, rgba(255,255,255,0) 100%); 255 | background: linear-gradient(to right, rgba(255,255,255,0) 0%, rgba(255,255,255,0.9) 25%, rgba(255,255,255,0.9) 75%, rgba(255,255,255,0) 100%); 256 | } 257 | 258 | 259 | 260 | /* 261 | * FixedColumns styles 262 | */ 263 | div.DTFC_LeftHeadWrapper table, 264 | div.DTFC_LeftFootWrapper table, 265 | div.DTFC_RightHeadWrapper table, 266 | div.DTFC_RightFootWrapper table, 267 | table.DTFC_Cloned tr.even { 268 | background-color: white; 269 | margin-bottom: 0; 270 | } 271 | 272 | div.DTFC_RightHeadWrapper table , 273 | div.DTFC_LeftHeadWrapper table { 274 | border-bottom: none !important; 275 | margin-bottom: 0 !important; 276 | border-top-right-radius: 0 !important; 277 | border-bottom-left-radius: 0 !important; 278 | border-bottom-right-radius: 0 !important; 279 | } 280 | 281 | div.DTFC_RightHeadWrapper table thead tr:last-child th:first-child, 282 | div.DTFC_RightHeadWrapper table thead tr:last-child td:first-child, 283 | div.DTFC_LeftHeadWrapper table thead tr:last-child th:first-child, 284 | div.DTFC_LeftHeadWrapper table thead tr:last-child td:first-child { 285 | border-bottom-left-radius: 0 !important; 286 | border-bottom-right-radius: 0 !important; 287 | } 288 | 289 | div.DTFC_RightBodyWrapper table, 290 | div.DTFC_LeftBodyWrapper table { 291 | border-top: none; 292 | margin: 0 !important; 293 | } 294 | 295 | div.DTFC_RightBodyWrapper tbody tr:first-child th, 296 | div.DTFC_RightBodyWrapper tbody tr:first-child td, 297 | div.DTFC_LeftBodyWrapper tbody tr:first-child th, 298 | div.DTFC_LeftBodyWrapper tbody tr:first-child td { 299 | border-top: none; 300 | } 301 | 302 | div.DTFC_RightFootWrapper table, 303 | div.DTFC_LeftFootWrapper table { 304 | border-top: none; 305 | margin-top: 0 !important; 306 | } 307 | 308 | #container .table.dataTable tbody tr.selected { 309 | background-color: #f2f5ca; 310 | } 311 | 312 | /* 313 | * FixedHeader styles 314 | */ 315 | div.FixedHeader_Cloned table { 316 | margin: 0 !important 317 | } 318 | 319 | -------------------------------------------------------------------------------- /static/css/datatables.responsive.css: -------------------------------------------------------------------------------- 1 | table.dataTable.dtr-inline.collapsed tbody td:first-child, 2 | table.dataTable.dtr-inline.collapsed tbody th:first-child { 3 | position: relative; 4 | padding-left: 30px; 5 | cursor: pointer; 6 | } 7 | table.dataTable.dtr-inline.collapsed tbody td:first-child:before, 8 | table.dataTable.dtr-inline.collapsed tbody th:first-child:before { 9 | top: 8px; 10 | left: 4px; 11 | height: 16px; 12 | width: 16px; 13 | display: block; 14 | position: absolute; 15 | color: white; 16 | border: 2px solid white; 17 | border-radius: 16px; 18 | text-align: center; 19 | line-height: 14px; 20 | box-shadow: 0 0 3px #444; 21 | box-sizing: content-box; 22 | content: '+'; 23 | background-color: #31b131; 24 | } 25 | table.dataTable.dtr-inline.collapsed tbody td:first-child.dataTables_empty:before, 26 | table.dataTable.dtr-inline.collapsed tbody th:first-child.dataTables_empty:before { 27 | display: none; 28 | } 29 | table.dataTable.dtr-inline.collapsed tbody tr.parent td:first-child:before, 30 | table.dataTable.dtr-inline.collapsed tbody tr.parent th:first-child:before { 31 | content: '-'; 32 | background-color: #d33333; 33 | } 34 | table.dataTable.dtr-inline.collapsed tbody tr.child td:before { 35 | display: none; 36 | } 37 | table.dataTable.dtr-column tbody td.control, 38 | table.dataTable.dtr-column tbody th.control { 39 | position: relative; 40 | cursor: pointer; 41 | } 42 | table.dataTable.dtr-column tbody td.control:before, 43 | table.dataTable.dtr-column tbody th.control:before { 44 | top: 50%; 45 | left: 50%; 46 | height: 16px; 47 | width: 16px; 48 | margin-top: -10px; 49 | margin-left: -10px; 50 | display: block; 51 | position: absolute; 52 | color: white; 53 | border: 2px solid white; 54 | border-radius: 16px; 55 | text-align: center; 56 | line-height: 14px; 57 | box-shadow: 0 0 3px #444; 58 | box-sizing: content-box; 59 | content: '+'; 60 | background-color: #31b131; 61 | } 62 | table.dataTable.dtr-column tbody tr.parent td.control:before, 63 | table.dataTable.dtr-column tbody tr.parent th.control:before { 64 | content: '-'; 65 | background-color: #d33333; 66 | } 67 | table.dataTable tr.child { 68 | padding: 0.5em 1em; 69 | } 70 | table.dataTable tr.child:hover { 71 | background: transparent !important; 72 | } 73 | table.dataTable tr.child ul { 74 | display: inline-block; 75 | list-style-type: none; 76 | margin: 0; 77 | padding: 0; 78 | } 79 | table.dataTable tr.child ul li { 80 | border-bottom: 1px solid #efefef; 81 | padding: 0.5em 0; 82 | } 83 | table.dataTable tr.child ul li:first-child { 84 | padding-top: 0; 85 | } 86 | table.dataTable tr.child ul li:last-child { 87 | border-bottom: none; 88 | } 89 | table.dataTable tr.child span.dtr-title { 90 | display: inline-block; 91 | min-width: 75px; 92 | font-weight: bold; 93 | } 94 | -------------------------------------------------------------------------------- /static/css/font-awesome.min.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome 4.6.3 by @davegandy - http://fontawesome.io - @fontawesome 3 | * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) 4 | */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot');src:url('../fonts/fontawesome-webfont.eot') format('embedded-opentype'),url('../fonts/fontawesome-webfont.woff2') format('woff2'),url('../fonts/fontawesome-webfont.woff') format('woff'),url('../fonts/fontawesome-webfont.ttf') format('truetype'),url('../fonts/fontawesome-webfont.svg') format('svg');font-weight:normal;font-style:normal}.fa{display:inline-block;font:normal normal normal 14px/1 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for(i=0;ivItem2.cz) 138 | { 139 | return -1; 140 | } 141 | else if(vItem1.cz 2 | 3 | 4 | 5 | 6 | 7 | 基于金融知识图谱的知识计算引擎 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 181 | 182 | 270 | 271 | 272 | -------------------------------------------------------------------------------- /templates/dialogue.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 基于金融知识图谱的知识计算引擎 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 190 | 191 | 192 | 229 | 230 | 231 | 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基于金融知识图谱的知识计算引擎

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