In the intelligent era, we have become accustomed to letting AI provide us with various services. 

When you ask DeepSeek: "What's the weather like in Beijing?", it can give an accurate answer; when you ask ChatGPT to "explain the theory of relativity in simple language", it can answer fluently... 

Behind these AI capabilities, in addition to the perception capabilities of neural networks, there is also a key supporting technology - knowledge graph .

Knowledge Graph: Machine Cognitive Map

Knowledge graph is essentiallyUse association diagrams to represent objects and relationships in the real world.

For example, when you open an encyclopedia, the knowledge points in it are isolated from each other. The knowledge graph connects these knowledge points through relationships to formMesh structure.

In the knowledge graph,Nodes represent entities(such as China, Beijing, population),Edges represent relationships(such as "the capital is", "has").

This structure allows AI to not only know independent facts, but also understand the connections between things. When you ask "where is the capital of China", AI can directly find the answer through the knowledge graph; when you further ask "what is the population of Beijing", AI can find the answer through the knowledge graph.Relationship Networkturn upRelated information.

The knowledge graph is composed of triplets: entity-relationship-entity . This structure is similar to human cognition, allowing machines to understand the world more naturally. Unlike the table storage of traditional databases, the knowledge graph organizes information into a network to facilitate reasoning and discovery of implicit relationships.

When we say "AI has knowledge", it actually means that AI can access and utilize the structured information stored in the knowledge graph. This information is carefully organized to enable AI to carry out human-like thinking processes.

Building the knowledge architecture of AI

Building a knowledge graph is like creating a cognitive map for AI. This process involves multiple steps:Data collection, data cleaning, structure design, graph construction, graph computing and application deployment.

Data is the foundation of knowledge graphIn the field of financial risk control, we need to collect multi-dimensional data such as customer mobile phone numbers, bank cards, equipment information, unit data, location information, etc. These data points will become nodes or attributes in the graph. Data quality directly affects the graph effect, so data cleaning is crucial.

Structural design determines how the knowledge graph is organized. There are two common structures :

Implicit application node structureEmphasizes the concise presentation of customer relationships, with high computational efficiency, and customer relationships only need to be calculated twice. Suitable for simple business models, such as single credit approval scenarios.

Explicit Application Node StructureTreating application behavior as an independent node can clearly show the customer's multiple application behavior, which is suitable for complex business scenarios such as revolving credit and multiple use of funds. This structure requires four-dimensional calculation to calculate customer associations, which consumes a lot of resources.

After the graph is built, we can use the community discovery algorithm to identify closely connected customer clusters.Centrality AlgorithmDiscover key nodes and use the shortest path algorithm to analyze the most closely related paths between customers. These graph computing capabilities are difficult to achieve with traditional databases.

Knowledge graph: the key battlefield for AI empowerment

Knowledge graphs have shown great value in many fields.From financial risk control to healthcare, from intelligent search to AI integration, it is becoming the core engine of digital transformation..

Financial risk control field,The knowledge graph can efficiently identify fraud gangs by building a customer relationship network.

An example scenario: 18 applicants are closely connected through multiple relationships such as devices and phones, and 13 of them are overdue, with an overdue rate of up to 72.2%. Traditional databases require multiple full scans to find such associated clusters, but knowledge graphs can do it in seconds. Knowledge graphs can also identify core nodes in the gang and discover possible intermediary fraud.

Medical and health field, the knowledge graph connects symptoms, diseases, treatments and drugs to support intelligent diagnosis and treatment.

Interestingly, Baidu Lingyi Zhihui system has realized intelligent consultation through knowledge graph, and its diagnosis accuracy is comparable to that of human doctors, and the similarity between prescriptions and those prescribed by traditional Chinese medicine practitioners can reach 80-90%. This shows that AI can already transform structured medical knowledge into clinical decision support.

Smart search,Knowledge graph is the core technology for the revolutionary upgrade of search engines such as Google and Baidu.

When you search for "Jay Chou's wife", the search engine no longer just matches the keywords, but understands that "Jay Chou" is a person and "wife" is a relationship, and directly gives the answer "Kun Ling".

The most exciting thing isCombination of knowledge graph and large language model.

The current LLM mainly solves the perception problem, while the knowledge graph, as a representative of the symbolic school, can provide AI with structured world knowledge and reasoning capabilities. Professor Geoffrey Hinton of the University of Toronto pointed out that one of the future development directions of AI is the in-depth combination of deep neural networks and symbolic artificial intelligence. Knowledge graphs are becoming an important cornerstone towards general artificial intelligence (AGI).

In essence, knowledge graph is AI from "Perceptual Intelligence"Towards"Cognitive Intelligence" is a key step in the development of the Internet of Things. It not only provides a large amount of structured knowledge, but more importantly, it provides an associative reasoning method similar to human thinking.

With the development of knowledge graph technology, we will see significant progress in AI's advanced cognitive functions such as understanding, reasoning, and association, and ultimately achieve true intelligence.AGI.