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知识图谱是由 Google 公司在 2012 年提出来的一个新的概念。从学术的角度,我们可以对知识图谱给一个这样的定义:“知识图谱本质上是语义网络(Semantic Network)的知识库”。
Graph:图(Graph)是由节点(Vertex)和边(Edge)来构成。多关系图一般包含多种类型的节点和多种类型的边。实体(节点)指的是现实世界中的事物比如人、地名、概念、药物、公司等,关系(边)则用来表达不同实体之间的某种联系。
Schema:限定待加入知识图谱数据的格式;相当于某个领域内的数据模型,包含了该领域内有意义的概念类型以及这些类型的属性
先安装好neo4j与jdk,安装并配置环境变量,具体见一个链接
在cmd输入以下命令以启动,启动完毕在浏览器输入http://localhost:7474/访问
neo4j.bat console
MATCH (n) DETACH DELETE n
MATCH是匹配操作,而小括号()代表一个节点node(可理解为括号类似一个圆形),括号里面的n为标识符。
CREATE (n:Person {name:'John'}) RETURN n
CREATE是创建操作,Person是标签,代表节点的类型。
花括号{}代表节点的属性,属性类似Python的字典。
这条语句的含义就是创建一个标签为Person的节点,该节点具有一个name属性,属性值是John。
- CREATE (n:Person {name:'Sally'}) RETURN n;
- CREATE (n:Person {name:'Steve'}) RETURN n;
- CREATE (n:Person {name:'Mike'}) RETURN n;
- CREATE (n:Person {name:'Liz'}) RETURN n;
- CREATE (n:Person {name:'Shawn'}) RETURN n;
分号;代表分开一句句运行
- CREATE (n:Location {city:'Miami', state:'FL'})
- CREATE (n:Location {city:'Boston', state:'MA'})
- CREATE (n:Location {city:'Lynn', state:'MA'})
- CREATE (n:Location {city:'Portland', state:'ME'})
- CREATE (n:Location {city:'San Francisco', state:'CA'})
节点类型为Location,属性包括city和state
- MATCH (a:Person {name:'Liz'}),
- (b:Person {name:'Mike'})
- MERGE (a)-[:FRIENDS]->(b)
方括号[]即为关系,FRIENDS为关系的类型。
注意这里的箭头–>是有方向的,表示是从a到b的关系。 这样,Liz和Mike之间建立了FRIENDS关系。
- MATCH (a:Person {name:'Shawn'}),
- (b:Person {name:'Sally'})
- MERGE (a)-[:FRIENDS {since:2001}]->(b)
- MATCH (a:Person {name:'Shawn'}), (b:Person {name:'John'}) MERGE (a)-[:FRIENDS {since:2012}]->(b)
- MATCH (a:Person {name:'Mike'}), (b:Person {name:'Shawn'}) MERGE (a)-[:FRIENDS {since:2006}]->(b)
- MATCH (a:Person {name:'Sally'}), (b:Person {name:'Steve'}) MERGE (a)-[:FRIENDS {since:2006}]->(b)
- MATCH (a:Person {name:'Liz'}), (b:Person {name:'John'}) MERGE (a)-[:MARRIED {since:1998}]->(b)
- MATCH (a:Person {name:'John'}), (b:Location {city:'Boston'}) MERGE (a)-[:BORN_IN {year:1978}]->(b)
- MATCH (a:Person {name:'Liz'}), (b:Location {city:'Boston'}) MERGE (a)-[:BORN_IN {year:1981}]->(b)
- MATCH (a:Person {name:'Mike'}), (b:Location {city:'San Francisco'}) MERGE (a)-[:BORN_IN {year:1960}]->(b)
- MATCH (a:Person {name:'Shawn'}), (b:Location {city:'Miami'}) MERGE (a)-[:BORN_IN {year:1960}]->(b)
- MATCH (a:Person {name:'Steve'}), (b:Location {city:'Lynn'}) MERGE (a)-[:BORN_IN {year:1970}]->(b)
CREATE (a:Person {name:'Todd'})-[r:FRIENDS]->(b:Person {name:'Carlos'})
MATCH (a:Person)-[:BORN_IN]->(b:Location {city:'Boston'}) RETURN a,b
MATCH (a)--() RETURN a
MATCH (a)-[r]->() RETURN a.name, type(r)
MATCH (a)-[r]->() RETURN a.name, type(r)
MATCH (n)-[:MARRIED]-() RETURN n
MATCH (a:Person {name:'Mike'})-[r1:FRIENDS]-()-[r2:FRIENDS]-(friend_of_a_friend) RETURN friend_of_a_friend.name AS fofName
- MATCH (a:Person {name:'Liz'}) SET a.age=34
- MATCH (a:Person {name:'Shawn'}) SET a.age=32
- MATCH (a:Person {name:'John'}) SET a.age=44
- MATCH (a:Person {name:'Mike'}) SET a.age=25
- MATCH (a:Person {name:'Mike'}) SET a.test='test'
- MATCH (a:Person {name:'Mike'}) REMOVE a.test
MATCH (a:Location {city:'Portland'}) DELETE a
MATCH (a:Person {name:'Todd'})-[rel]-(b:Person) DELETE a,b,rel
- # step 1:导入 Neo4j 驱动包
- from neo4j import GraphDatabase
- # step 2:连接 Neo4j 图数据库
- driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
- # 添加 关系 函数
- def add_friend(tx, name, friend_name):
- tx.run("MERGE (a:Person {name: $name}) "
- "MERGE (a)-[:KNOWS]->(friend:Person {name: $friend_name})",
- name=name, friend_name=friend_name)
- # 定义 关系函数
- def print_friends(tx, name):
- for record in tx.run("MATCH (a:Person)-[:KNOWS]->(friend) WHERE a.name = $name "
- "RETURN friend.name ORDER BY friend.name", name=name):
- print(record["friend.name"])
- # step 3:运行
- with driver.session() as session:
- session.write_transaction(add_friend, "Arthur", "Guinevere")
- session.write_transaction(add_friend, "Arthur", "Lancelot")
- session.write_transaction(add_friend, "Arthur", "Merlin")
- session.read_transaction(print_friends, "Arthur")
上述程序的核心部分,抽象一下就是:neo4j.GraphDatabase.driver(xxxx).session().write_transaction(函数(含tx.run(CQL语句)))
neo4j.GraphDatabase.driver(xxxx).session().begin_transaction.run(CQL语句)
- # step 1:导包
- from py2neo import Graph, Node, Relationship
- gragh = Graph('bolt://localhost:7687', auth = ('username', 'password'))
- # step 2:构建图
- g = Graph()
- # step 3:创建节点
- tx = g.begin()
- a = Node("Person", name="Alice")
- tx.create(a)
- b = Node("Person", name="Bob")
- # step 4:创建边
- ab = Relationship(a, "KNOWS", b)
- # step 5:运行
- tx.create(ab)
- tx.commit()
csv分为两个nodes.csv和relations.csv,注意关系里的起始节点必须是在nodes.csv里能找到的
- # nodes.csv需要指定唯一ID和nam,
- headers = [
- 'unique_id:ID', # 图数据库中节点存储的唯一标识
- 'name', # 节点展示的名称
- 'node_type:LABEL', # 节点的类型,比如Person和Location
- 'property' # 节点的其他属性
- ]
- # relations.csv
- headers = [
- 'unique_id', # 图数据库中关系存储的唯一标识
- 'begin_node_id:START_ID', # begin_node和end_node的值来自于nodes.csv中节点
- 'end_node_id:END_ID',
- 'begin_node_name',
- 'end_node_name',
- 'begin_node_type',
- 'end_node_type',
- 'relation_type:TYPE', # 关系的类型,比如Friends和Married
- 'property' # 关系的其他属性
- ]
neo4j安装绝对路径/import
neo4j bin/neo4j-admin import --nodes=/var/lib/neo4j/import/nodes.csv --relationships=/var/lib/neo4j/import/relas.csv --delimiter=^ --database=xinfang*.db
修改 /root/neo4j/conf/neo4j.conf 文件中的 dbms.default_database=mygraph.db
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