The emergence of knowledge graphs is changing the way we organize and understand information. 

This technology is not only a way of storing data, but also a change in thinking mode.From isolated information points to interconnected knowledge networks.

Knowledge graphs and traditional knowledge bases: Deconstructing the philosophy of data connection

Traditional enterprise databases and knowledge bases have long been consideredInformation warehouse,

They are like separate drawers, each of which neatly stores a specific type of information. Enterprises are used to this storage method, but rarely think: Do we really need to put information in different drawers?

The knowledge graph breaks this traditional thinking. It regards information as a huge network, where each piece of information is a node in the network.Nodes are connected by various relationships.

In the knowledge graph, "Einstein" is not just a record, but a person with multiple entities such as "Relativity", "Nobel Prize", and "Princeton University".Rich associated nodesThis structure makes data no longer static, but a dynamic knowledge network.

The essential difference between traditional knowledge base and knowledge graph isThree Dimensions:

Structural Dimensions: Fixed Model vs Flexible Network

Traditional knowledge bases use a predefined table structure, just like a pre-built bookshelf, where each book must be placed in a specific position. This structure is efficient when dealing with established information, but it is rigid when faced with changing information relationships.

The knowledge graph is like a map that can be expanded arbitrarily.Spider Web, each knowledge point can be connected to any other point. This flexibility enables complex and changing knowledge relationships to be expressed naturally.

Semantic Dimension: Limited Description vs. Rich Association

The JOIN operation in traditional databases can only express simple associations, just like "Zhang San is Li Si's colleague".

In the knowledge graph, the relationship itself is also a describable entity, which can express "Zhang San and Li Si were college classmates and founded a company together in 2012Such rich semantic information.

The commercial value of knowledge graphs: the revolution brought by connections

Knowledge graph is not only a technology, but also aBusiness ThinkingIt connects scattered data points to unlock the hidden power of isolated information.Great value.

In Google search engines, knowledge graphs make search results no longer just a list of related web pages, but can directly displayStructured content such as biography, company information, product details, etc.This change not only improves the user experience, but also creates a new business model.

Netflix's recommendation system uses knowledge graphs to understand the relationship between movies. It is not just based on the simple relationship of "people who have watched this movie also watched that movie", but canunderstandThe relationship between plot, actors, directors, styles and other dimensions provides more accuratePersonalized recommendations.

In the financial field, knowledge graphs have completely changedRisk Control Mode.

Traditional risk control can only make judgments based on the historical behavior of a single customer, while knowledge graphs canDiscover hidden networks of connections between customersWhen an applicant has complex equipment sharing and mobile phone number associations with multiple overdue customers, the system can identify potential risks even if the applicant has good credit.

After a large bank applied the knowledge graph, the fraud gang identification rate increased by 25%, and the bank recovered more than 300 million yuan in losses each year.

Knowledge Graph or Traditional Database? A Practical Guide

In the face of specific business scenarios, how do you decide whether to choose a knowledge graph or stick with a traditional relational database? Here is a practical judgment framework:

When you need to deal with complex relationship networks , the advantages of knowledge graphs are obvious.

Instead of creating dozens of tables and writing complex JOIN queries in traditional databases, it is better to take advantage of the natural advantages of graph databases.Express and query these relationships directly.

In the risk control system of a financial institution, 36 correlation matches were originally required to detect secondary correlation fraud risks. However, after using the knowledge graph, this process was simplified to a few basic operations, and the efficiency was increased by more than ten times.

Knowledge graphs are best suited forScenario:

Relationship analysis scenario : When you need to understand the complex relationships between entities, such as social network analysis, fraud gang identification, and supply chain relationship mining, the knowledge graph can intuitively display the various connections between entities.

Path query scenario : Knowledge graphs can efficiently find the shortest path or all possible paths between two entities, such as finding the link between a scientist and a research result, or tracking the flow of funds in a financial transaction.

Community discovery scenario : When identifying closely connected groups of entities, knowledge graphs provide mature solutions such as the Louvain algorithm and label propagation algorithm, which can quickly discover natural clusters in the data.

In contrast, traditional relational databases still have advantages in the following scenarios:Irreplaceable advantages:

High-concurrency transaction processing : Scenarios that require high-concurrency processing of simple transactions, such as the order system of e-commerce websites and the core accounting system of banks.

Business with fixed structure : When the data structure is relatively stable and the data model does not need to be changed frequently.

Single entity attribute query : When the business is mainly concerned with the attributes of a single entity rather than the complex relationships between entities.

In the real world, many systems choose two technologiesParallel use, giving full play to their respective advantages. For example, an e-commerce recommendation system may use a relational database to store basic information such as orders and products, and use a knowledge graph to analyze user interest networks and product associations to provide more accurate personalized recommendations.

Knowledge graphs are not meant to replace traditional databases , but to provide a new way of thinking and technical means that allow us to more naturally express and understand the complex interconnected world. Just like Einstein's theory of relativity did not invalidate Newtonian mechanics, but provided a more accurate explanation framework in specific scenarios.

Today, data connections create value. Knowledge graphs are becoming a new engine for connecting the world, helping us discover new insights from massive amounts of data.Hidden patterns, relationships, and insights, creating unprecedented business value.