Survey of Knowledge Graph Approaches and Applications

TLDR

Knowledge graph technology, a key AI component, enables extraction of structured knowledge from massive data and supports intelligent services, yet its theory and application technologies still require further research. This paper reviews the key technologies of knowledge graph construction and typical applications, and outlines future development directions and challenges. It examines the research progress of four core technologies—knowledge extraction, representation, fusion, and reasoning—within knowledge graph construction.

Abstract

With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been applied to various systems, the basic theory and application technology still need further research. On the basis of comprehensively expounding the definition and architecture of knowledge graph, this paper reviews the key technologies of knowledge graph construction, including the research progress of four core technologies such as knowledge extraction technology, knowledge representation technology, knowledge fusion technology and knowledge reasoning technology, as well as some typical applications. Finally, the future development direction and challenges of the knowledge graph are prospected.