Table of contents
This article deeply explores the core concepts, development history, research content, and technical details of knowledge graphs in terms of representation, storage, acquisition, construction, and reasoning. Combining Python and PyTorch sample code, the article aims to provide readers with a comprehensive, in-depth and practical overview of the knowledge graph, and to help technology enthusiasts and researchers deepen their understanding of this field.
Follow TechLead and share all-dimensional knowledge of AI. The author has 10+ years of Internet service architecture, AI product research and development experience, and team management experience. He holds a master's degree from Tongji University in Fudan University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and research and development of AI products with revenue of hundreds of millions. principal.
1 Overview
As a special information representation technology, knowledge graph has been reflected in various application fields in recent years, especially in natural language processing (NLP), its importance has become increasingly prominent. Knowledge graphs can store and manage large amounts of information in an efficient and organized manner, and can express the relationships between this information in the form of graphs, making the information more contextual and easier to understand and apply.
What is a knowledge graph
Definition : A knowledge graph is a structured information database in which the information is organized in the form of a graph. Each node represents an entity and each edge represents the relationship between two entities.
Example : Consider a scenario where we have a music knowledge graph. The nodes may include "The Beatles", "rock music" and "1960s", while the edges may indicate that "The Beatles" is a representative of "rock music", and that "The Beatles" is a representative of "rock music" in the "1960s" "very popular.
The relationship between knowledge graph and natural language processing
Definition : In natural language processing, knowledge graphs are used as a tool to help machines better understand and process natural language. Through knowledge graphs, machines can understand entities and their relationships in text, thereby making more accurate decisions or generating more accurate responses.
Example : Consider a question answering system. When a user asks "Which music style are the Beatles a representative of?", the system can query the knowledge graph and get "rock music" as the answer. This is because the relationship between "The Beatles" and "rock music" has been stored in the knowledge graph.
Overall, the knowledge graph provides a structured information source for natural language processing, which can greatly improve its performance and accuracy. With more research and applications, we can expect that the role of knowledge graphs in natural language processing will become increasingly important.
2. Development process
The concept of knowledge graph is not new, but in recent years due to technological advancement and the rise of big data, it has received unprecedented attention and development. From early semantic networks and ontologies to current large-scale commercial applications, the development of knowledge graphs has never stopped.
semantic network
Definition : Semantic network originated in the 1960s as a method of graphical representation of knowledge, in which nodes represent concepts and edges represent relationships between concepts.
Example : Consider a simple semantic network about animals. The nodes include "bird" and "penguin", and the edges indicate that "penguin" is a type of "bird". But at the same time, there might be another side that says "penguins" can't fly.
Ontology
Definition : Ontology in computer science is a method of formally describing knowledge in a specific domain. It not only describes entities and the relationships between them, but also includes rules about these entities and relationships.
Example : In medicine, ontologies can be used to describe various diseases, symptoms, and treatments. For example, it might have a rule that says: "If a person has symptoms A, B, and C, he or she most likely has disease X."
Knowledge graph in the era of big data
Definition : With the popularization of the Internet and the advancement of big data technology, knowledge graphs have begun to be used in more complex scenarios, such as search engines, intelligent assistants, and recommendation systems.
Example : Google’s “Knowledge Graph” is a well-known application that helps search engines understand user queries and provide relevant, structured information. For example, when you search for "Albert Einstein", you will not only get Wikipedia links about him, but also structured information about his life, achievements, related people, etc.
Integration of knowledge graph and deep learning
Definition : In recent years, the combination of knowledge graph and deep learning technology has become a hot research topic, in which knowledge graph provides structured background knowledge for deep learning models.
Example : In the field of drug discovery, knowledge graphs can describe entities such as compounds, diseases, and proteins and the relationships between them. Combined with deep learning, researchers can predict the relationship between new and unknown drugs and diseases, thus accelerating the drug development process.
In general, the development history of knowledge graph reflects the continuous progress of technology and applications. From early theoretical research to current commercial applications, it has always been at the forefront of knowledge representation and management.
3. Research content
With the rapid development of the field of knowledge graph, its research content has become increasingly rich and diverse. Some core research directions and related concept definitions are listed below.
Modeling and representation of knowledge graphs
Definition : The modeling and representation of knowledge graphs focuses on how to effectively organize, define and express entities and relationships in knowledge to facilitate computer processing and understanding.
Example : Resource Description Framework (RDF) is a knowledge graph representation standard that uses triples (subject, predicate, object) to express relationships between entities, such as: (Paris, is, the capital of France).
knowledge extraction
Definition : Knowledge extraction is to automatically extract valuable knowledge information from unstructured or semi-structured data sources (such as text, images, or audio) and add it to the knowledge graph.
Example : Automatically identify and extract key people, events, and locations from news articles, and then add this information to the existing knowledge graph.
Fusion and alignment of knowledge graphs
Definition : When faced with knowledge graphs from multiple sources or fields, the fusion and alignment of knowledge graphs focuses on how to integrate this knowledge to ensure its consistency and completeness.
Example : Two knowledge graphs about medicine may have partially overlapping content, but there are differences in the naming or classification of diseases. By aligning these two maps, a more complete and accurate medical knowledge base can be generated.
Reasoning about knowledge graphs
Definition : Use existing knowledge in the knowledge graph to perform logical reasoning to obtain new and implicit knowledge information.
Example : If the knowledge graph indicates "A is the father of B" and "B is the father of C", through reasoning, we can conclude that "A is the grandfather of C".
Evaluation and verification of knowledge graphs
Definition : In order to ensure the quality and accuracy of the knowledge graph, it needs to be evaluated and verified to check whether its content is accurate, complete and consistent.
Example : After adding new knowledge to the graph, the system may automatically compare it with the existing knowledge base to detect whether there is conflict or contradictory information.
In general, the content of knowledge graph research covers all aspects from knowledge representation to knowledge application, and its depth and breadth are constantly expanding, laying a solid foundation for future technological progress and applications.
4. Knowledge graph representation and storage
The representation and storage of a knowledge graph is key to ensuring its efficient use, as this determines how knowledge is queried, updated, and extended. Below we will delve into the representation and storage technology of knowledge graphs.
RDF: a representation method for knowledge graphs
Definition : Resource Description Framework (RDF) is a standard knowledge graph representation method that uses triples to describe entities and relationships in knowledge.
Example :
An RDF triple can be represented as:
(巴黎, 是, 法国的首都)
Python code :
# 一个简单的RDF三元组表示
triplet = ('巴黎', '是', '法国的首都')
print(triplet)
Storage: using a graph database
Definition : A graph database is a database designed for storing and querying graph-structured data. Due to its natural graph structure characteristics, knowledge graphs are very suitable for the storage and query methods of graph databases.
Example : Neo4j is a popular graph database that can be used to store and query knowledge graphs.
Python code : (Here we use py2neothe library, which is a Python client for Neo4j)
from py2neo import Graph, Node, Relationship
# 连接到Neo4j数据库
graph = Graph("http://localhost:7474", username="neo4j", password="password")
# 创建节点
paris = Node("City", name="巴黎")
france = Node("Country", name="法国")
# 创建关系
capital_relation = Relationship(paris, "是", france, description="法国的首都")
# 将节点和关系添加到图数据库中
graph.create(capital_relation)
Embedding: Knowledge representation using deep learning
Definition : Embedding is to represent the entities and relationships in the knowledge graph as low-dimensional vectors. This representation method uses deep learning models, such as TransE, to encode knowledge.
Example : Embed the two entities "Paris" and "Yes" into a vector space of dimension 10.
PyTorch code :
import torch
import torch.nn as nn
class EmbeddingModel(nn.Module):
def __init__(self, vocab_size, embedding_dim):
super(EmbeddingModel, self).__init__()
self.embeddings = nn.Embedding(vocab_size, embedding_dim)
def forward(self, input_ids):
return self.embeddings(input_ids)
# 假设我们的词汇表大小为1000，嵌入维度为10
model = EmbeddingModel(1000, 10)
# 获取"巴黎"和"是"的嵌入向量
# 这里我们仅为示例，随机指定"巴黎"和"是"的id为5和10
paris_embedding = model(torch.tensor([5]))
is_embedding = model(torch.tensor([10]))
print(paris_embedding)
print(is_embedding)
Summary: The representation and storage of knowledge graphs is one of its core technologies, ensuring efficient query and update of knowledge. From traditional RDF representation to modern deep learning embedding methods, this field is always evolving and innovating.
5. Acquisition and construction of knowledge graph
The acquisition and construction of knowledge graphs is the core part of knowledge graph research, focusing on how to automatically or semi-automatically extract and integrate knowledge from various data sources, and form a structured knowledge graph.
knowledge extraction
Definition : Knowledge extraction is the process of automatically identifying and extracting entities, relationships and events from unstructured or semi-structured data.
Example : Extract the information "Steve Jobs is the founder of Apple" from an article introducing Steve Jobs.
Python code : (The Spacy library is used here for simple named entity recognition)
import spacy
# 加载模型
nlp = spacy.load("en_core_web_sm")
text = "Steve Jobs was the co-founder of Apple."
doc = nlp(text)
# 抽取实体
for ent in doc.ents:
print(ent.text, ent.label_)
knowledge integration
Definition : Knowledge fusion is the integration of knowledge from multiple knowledge sources, eliminating conflicts and redundancies, and forming a unified and consistent knowledge graph.
Example : Get "Steve Jobs, founder of Apple" and "Jobs, co-founder of Apple" from two databases respectively, and integrate them into "Steve Jobs is the co-founder of Apple".
Python code : (simplified fusion example)
knowledge1 = {
"name": "史蒂夫·乔布斯", "title": "Apple创始人"}
knowledge2 = {
"name": "乔布斯", "title": "苹果公司联合创始人"}
def fuse_knowledge(k1, k2):
fused_knowledge = {
}
fused_knowledge["name"] = k1["name"] # 选择更全的名称
# 合并title，简化为选择k2的title
fused_knowledge["title"] = k2["title"]
return fused_knowledge
result = fuse_knowledge(knowledge1, knowledge2)
print(result)
Knowledge verification
Definition : Knowledge verification is to check whether the information in the knowledge graph is accurate, consistent and reliable to ensure its quality.
Example : Verify that "Steve Jobs is the founder of Microsoft" is correct.
Python code : (assuming we have a verified knowledge base to check this information)
validated_knowledge_base = {
"史蒂夫·乔布斯": "Apple的创始人",
"比尔·盖茨": "Microsoft的创始人"
}
def validate_knowledge(entity, claim):
if entity in validated_knowledge_base:
return validated_knowledge_base[entity] == claim
return False
is_valid = validate_knowledge("史蒂夫·乔布斯", "Microsoft的创始人")
print(is_valid) # 输出为False，因为此知识是错误的
The acquisition and construction of a knowledge graph is a complex and ongoing process involving multiple steps and technologies. The above code is only a simplified example. Real knowledge acquisition and construction will be more complicated, but the basic idea is similar.
6. Knowledge graph reasoning
Knowledge graph reasoning is one of the core research areas of knowledge graphs, which involves using entities and relationships in existing knowledge graphs to deduce and predict new relationships or attributes.
logical reasoning
Definition : Logical reasoning uses formal logic to derive new relationships or attributes in a knowledge graph, usually based on predefined rules or patterns.
Example : Given the following knowledge:
- All humans are living creatures.
- Tom is a person.
We can infer: Tom is a living thing.
Python code :
knowledge_base = {
"所有人": "生物",
"Tom": "人"
}
def logic_inference(entity):
if entity in knowledge_base:
if knowledge_base[entity] == "人":
return "生物"
return knowledge_base[entity]
return None
result = logic_inference("Tom")
print(result) # 输出：生物
knowledge embedded reasoning
Definition : Knowledge embedding reasoning uses deep learning models, such as TransE or TransH, to map entities and relationships in the knowledge graph to a low-dimensional vector space, and perform reasoning through vector operations.
Example : Given the knowledge "Beijing" - "is" -> "the capital of China", we can infer other similar relationships such as "Tokyo" - "is" -> "the capital of Japan".
PyTorch code :
import torch
import torch.nn as nn
import torch.optim as optim
# 使用TransE模型的简化版本
class TransE(nn.Module):
def __init__(self, entity_size, relation_size, embedding_dim):
super(TransE, self).__init__()
self.entity_embeddings = nn.Embedding(entity_size, embedding_dim)
self.relation_embeddings = nn.Embedding(relation_size, embedding_dim)
def forward(self, head, relation):
head_embedding = self.entity_embeddings(head)
relation_embedding = self.relation_embeddings(relation)
return head_embedding + relation_embedding
# 假设我们有3个实体和1个关系
model = TransE(3, 1, 10)
# 训练模型... (这里略过训练过程)
# 推理
beijing_id, is_id, tokyo_id = 0, 0, 2
predicted_tail = model(beijing_id, is_id)
actual_tail = model.entity_embeddings(torch.tensor(tokyo_id))
# 计算相似性
similarity = torch.nn.functional.cosine_similarity(predicted_tail, actual_tail)
print(similarity)
path reasoning
Definition : Path reasoning is to derive new relationships based on multi-hop relationships between entities in the knowledge graph.
Example : If we know "A is B's friend" and "B is C's friend", we can infer "A may know C".
Python code :
relations = {
"A": ["B"],
"B": ["C"]
}
def path_inference(entity):
friends = relations.get(entity, [])
friends_of_friends = []
for friend in friends:
friends_of_friends.extend(relations.get(friend, []))
return friends_of_friends
result = path_inference("A")
print(result) # 输出：['C']
Knowledge graph reasoning is a challenging field because it requires processing large amounts of knowledge and deriving new and useful information from it. The above methods and code provide an entry-level overview, and actual applications and research will be more complex.
Summarize
In the past few years, knowledge graph has gradually transformed from an academic concept into a powerful tool widely used in practical business scenarios. From the most basic concepts, development history, and research content, to the representation, storage, acquisition, construction, and reasoning of more complex knowledge graphs, we have gradually gained a deeper understanding of the technical connotations of this field.
However, looking at the entire development process of knowledge graph, one of the most prominent features is that knowledge graph is a field that continues to evolve . With the growth of data, advancement of technology, and expansion of application scenarios, the problems that knowledge graphs need to deal with are also continuing to change and expand.
In addition, I think there are two core insights worthy of further consideration:
-
Knowledge graph and human thinking : Knowledge graph is not only a tool for storing and managing knowledge, but more importantly, it simulates human thinking mode to some extent. How we organize, link and use knowledge is well reflected in the knowledge graph. Therefore, the study of knowledge graphs actually deepens our understanding of human cognition.
-
Balance between technology and application : The development of knowledge graphs should not just stop at the technical level. More importantly, how to apply these technologies to practical problems and maximize the use of knowledge. This requires us to constantly balance technology and applications to ensure that the technological progress of knowledge graphs can truly serve actual business needs.
Follow TechLead and share all-dimensional knowledge of AI. The author has 10+ years of Internet service architecture, AI product research and development experience, and team management experience. He holds a master's degree from Tongji University in Fudan University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and research and development of AI products with revenue of hundreds of millions. principal.