知识图谱：概念、代码示例、应用场景与注意事项

最新推荐文章于 2025-04-22 23:21:14 发布

原创最新推荐文章于 2025-04-22 23:21:14 发布

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#知识图谱 #人工智能

一、知识图谱的概念讲解

知识图谱是一种以图形结构表示知识的方法，它通过实体、关系和属性的组合来组织和表示信息。知识图谱的核心在于将知识中的实体（如人、地点、事件等）和它们之间的关系（如“出生于”、“位于”等）以图的形式表示出来，为机器理解和处理自然语言提供了丰富的语义信息。

二、知识图谱的代码示例

（一）使用RDFLib构建知识图谱

以下是一个使用Python的RDFLib库构建知识图谱的代码示例：

Python复制

from rdflib import Graph, Literal, RDF, URIRef
from rdflib.namespace import FOAF, XSD

# 创建一个知识图谱实例
g = Graph()

# 定义实体的URI
person1 = URIRef("http://example.org/person1")
person2 = URIRef("http://example.org/person2")
knows = URIRef("http://xmlns.com/foaf/0.1/knows")
name = URIRef("http://xmlns.com/foaf/0.1/name")
age = URIRef("http://xmlns.com/foaf/0.1/age")

# 向图中添加三元组（subject, predicate, object）
g.add((person1, RDF.type, FOAF.Person))
g.add((person1, name, Literal("Alice", datatype=XSD.string)))
g.add((person1, age, Literal(25, datatype=XSD.int)))

g.add((person2, RDF.type, FOAF.Person))
g.add((person2, name, Literal("Bob", datatype=XSD.string)))
g.add((person1, knows, person2))

# 输出图中的所有三元组
for subj, pred, obj in g:
    print(subj, pred, obj)

（二）使用LangChain和OpenAI生成知识图谱

以下是一个使用LangChain和OpenAI生成知识图谱的代码示例：

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from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.graphs.networkx_graph import KG_TRIPLE_DELIMITER
from pprint import pprint

# Prompt template for knowledge triple extraction
_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
    "You are a networked intelligence helping a human track knowledge triples"
    " about all relevant people, things, concepts, etc. and integrating"
    " them with your knowledge stored within your weights"
    " as well as that stored in a knowledge graph."
    " Extract all of the knowledge triples from the text."
    " A knowledge triple is a clause that contains a subject, a predicate,"
    " and an object. The subject is the entity being described,"
    " the predicate is the property of the subject that is being"
    " described, and the object is the value of the property.\n\n"
    "EXAMPLE\n"
    "It's a state in the US. It's also the number 1 producer of gold in the US.\n\n"
    f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
    f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\n"
    "END OF EXAMPLE\n\n"
    "EXAMPLE\n"
    "I'm going to the store.\n\n"
    "Output: NONE\n"
    "END OF EXAMPLE\n\n"
    "EXAMPLE\n"
    "Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\n"
    f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\n"
    "END OF EXAMPLE\n\n"
    "EXAMPLE\n"
    "{text}"
    "Output:"
)

KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
    input_variables=["text"],
    template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
)

llm = ChatOpenAI(temperature=0.9)

# Create an LLMChain using the knowledge triple extraction prompt
chain = LLMChain(llm=llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)

# Run the chain with the specified text
text = "The city of Paris is the capital and most populous city of France. The Eiffel Tower is a famous landmark in Paris."
triples = chain.invoke(
    {'text' : text}
).get('text')

pprint(triples)

def parse_triples(response, delimiter=KG_TRIPLE_DELIMITER):
    if not response:
        return []
    return response.split(delimiter)

triples_list = parse_triples(triples)

pprint(triples_list)