Financial knowledge query service interface design and implementation method based on knowledge graph

Abstract

The invention provides a financial knowledge query service interface design and implementation method based on a knowledge graph. The method comprises the steps of querying the financial entities andthe related attributes from the autonomously constructed financial knowledge graph; according to the knowledge of the financial field, defining the financial significance of three query interfaces, wherein the three query interfaces include the stock right association query, the multi-layer stock right query and the annular stock holding query; providing the algorithms of the three query interfaces; and realizing the three query interfaces by utilizing a knowledge graph related technology and a related graph query algorithm. According to the invention, the financial field and the knowledge graph are tightly combined, the complex financial relations are expressed in a knowledge graph form, and the advantages of the respective fields are exerted; the method is suitable for the query servicesof various financial fields and provides the sufficient knowledge association for the implementation of intelligent applications, the field knowledge is organized by taking the intelligent application requirements as cores, and the accurate and efficient field query services are provided.

Description

technical field [0001] The invention belongs to the field of computer application technology, and in particular relates to a method for designing and implementing a financial knowledge query service interface based on a knowledge map. Background technique [0002] The rise of big data and artificial intelligence has prompted the financial industry to transform into intelligent finance. Knowledge graph is a knowledge base with semantic processing and information interconnection capabilities, and it is an important part of artificial intelligence technology. It is essentially a graph-based data structure consisting of associated nodes and edges. In the knowledge graph, each node represents an "entity" that exists in the real world, and each edge is a "relationship" between entities. Knowledge graphs are the most effective representation of relationships. In layman's terms, a knowledge graph is a relational network obtained by connecting all different types of information to...

AI Technical Summary

Problems solved by technology

[0004] The analysis shows that the existing methods ignore the specific practical problems in the financial field, ignore the driving effect of intelligent applications on graph query algorithms, and the support ability of query algorithms for intelligent applications, and cannot guarantee that the query interface can accurately and quickly meet the final application. need

First of all, there are relatively few researches on queries in the financial field, especially equity relationships, and it is difficult to combine financial knowledge with graph algorithms on the basis of building knowledge graphs to meet changes in application requirements in real time.

In addition, existing methods ignore the combination of knowledge graphs and traditional algorithms, as well as the combination of the financial field and knowledge graphs, and more query is based on relational databases. Traditional relational database queries have problems, which is not conducive to analyzing the relationship between financial entities.

Due to the huge amount of financial data, if only relational databases are used, there will be many join operations between tables, it is difficult to express the complex equity relationship between enterprises, and the query efficiency is relatively slow

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