python构建知识图谱实战代码

构建知识图谱的代码实现分为以下几步：

1. 数据采集与清洗

2. 实体识别与关系抽取

3. 知识图谱建模

4. 知识图谱可视化

下面是一个简单的知识图谱构建实战代码：

# -*- coding: utf-8 -*-
import os
import json
import requests
from py2neo import Graph, Node, Relationship

# 设置neo4j数据库账号密码
graph = Graph("bolt://localhost:7687", username="neo4j", password="123456")

# API接口地址
url = "http://api.ltp-cloud.com/analysis/?api_key=<your_key>&text="

# 实体识别和关系抽取的类型
entity_type = ['nh', 'ni', 'ns', 'nt', 'nw']
relation_type = ['ATT', 'COO', 'VOB', 'SBV', 'FOB', 'POB', 'DBL', 'LAD', 'RAD', 'IS', 'HED']

# 定义实体节点类
class EntityNode(object):
    def __init__(self, name, type):
        self.name = name
        self.type = type

    def __hash__(self):
        return hash(self.name)

    def __eq__(self, other):
        return self.name == other.name and self.type == other.type

    def __repr__(self):
        return self.name

# 定义关系节点类
class RelationNode(object):
    def __init__(self, start_node, end_node, type):
        self.start_node = start_node
        self.end_node = end_node
        self.type = type

    def __hash__(self):
        return hash(self.start_node) + hash(self.end_node)

    def __eq__(self, other):
        return self.start_node == other.start_node and self.end_node == other.end_node and self.type == other.type

# 采集数据并进行清洗
def collect_data():
    data = []
    with open('data.txt', 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if line:
                data.append(line)
    return data

# 实体识别和关系抽取
def entity_relation_extraction(sentence):
    entities = []
    relations = []
    try:
        response = requests.get(url + sentence)
        result = json.loads(response.text.strip())['data'][0]
        for item in result:
            if item['ne'] in entity_type:
                entities.append(EntityNode(item['word'], item['ne']))
        for item in result:
            if item['relate'] in relation_type:
                start_node = EntityNode(item['gov'], item['gov_ne'])
                end_node = EntityNode(item['dep'], item['dep_ne'])
                relations.append(RelationNode(start_node, end_node, item['relate']))
    except:
        pass
    return entities, relations

# 知识图谱建模
def build_knowledge_graph(data):
    for sentence in data:
        entities, relations = entity_relation_extraction(sentence)
        for entity in entities:
            node = Node(entity.type, name=entity.name)
            graph.merge(node, entity.type, 'name')
        for relation in relations:
            start_node = Node(relation.start_node.type, name=relation.start_node.name)
            end_node = Node(relation.end_node.type, name=relation.end_node.name)
            graph.merge(start_node, relation.start_node.type, 'name')
            graph.merge(end_node, relation.end_node.type, 'name')
            rel = Relationship(start_node, relation.type, end_node)
            graph.merge(rel)

# 知识图谱可视化
def visualize_knowledge_graph():
    os.system("neo4j-admin set-initial-password 123456")
    os.system("neo4j start")
    os.system("neo4j stop")
    os.system("neo4j start")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON (n:nh) ASSERT n.name IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON (n:ni) ASSERT n.name IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON (n:ns) ASSERT n.name IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON (n:nt) ASSERT n.name IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON (n:nw) ASSERT n.name IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:ATT]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:COO]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:VOB]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:SBV]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:FOB]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:POB]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:DBL]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:LAD]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:RAD]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:IS]-() ASSERT r.type IS UNIQUE'")
    os.system("cypher-shell -u neo4j -p 123456 'CREATE CONSTRAINT ON ()-[r:HED]-() ASSERT r.type IS UNIQUE'")

if __name__ == '__main__':
    data = collect_data()
    build_knowledge_graph(data)
    visualize_knowledge_graph()

上述代码的实现过程是：

1. 采集数据并进行清洗，将每个句子存储在一个列表中。

2. 对每个句子进行实体识别和关系抽取，得到实体节点和关系节点。

3. 对实体节点和关系节点进行建模，使用py2neo库进行节点和关系的创建。

4. 对知识图谱进行可视化，使用neo4j数据库和cypher语言进行可视化。

其中，实体节点和关系节点的定义分别为EntityNode和RelationNode类，这两个类都包含了节点的名称和类型。在实体识别和关系抽取中，我们通过API接口对输入的句子进行分析，得到每个实体节点和关系节点的名称、类型和关系类型。在知识图谱建模中，我们首先判断节点是否存在，如果节点存在则不创建，否则创建新的节点。同时根据关系节点的起始节点和结束节点，创建关系。最后，我们使用neo4j数据库将知识图谱进行可视化。

需要注意的是，上述代码中的API接口需要替换为自己的接口地址，同时需要安装py2neo库和requests库。