Citation

Chen Y, Li H, Li H, Liu W, Wu Y, Huang Q, Wan S. J. Sens. Actuat. Netw. 2022; 11(4): e78.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/jsan11040078

PMID

unavailable

Abstract

In recent years, with the rapid development of Internet technology and applications, the scale of Internet data has exploded, which contains a significant amount of valuable knowledge. The best methods for the organization, expression, calculation, and deep analysis of this knowledge have attracted a great deal of attention. The knowledge graph has emerged as a rich and intuitive way to express knowledge. Knowledge reasoning based on knowledge graphs is one of the current research hot spots in knowledge graphs and has played an important role in wireless communication networks, intelligent question answering, and other applications. Knowledge graph-oriented knowledge reasoning aims to deduce new knowledge or identify wrong knowledge from existing knowledge. Different from traditional knowledge reasoning, knowledge reasoning methods oriented to knowledge graphs are more diversified due to the concise, intuitive, flexible, and rich knowledge expression forms in knowledge graphs. Based on the basic concepts of knowledge graphs and knowledge graph reasoning, this paper introduces the latest research progress in knowledge graph-oriented knowledge reasoning methods in recent years. Specifically, according to different reasoning methods, knowledge graph reasoning includes rule-based reasoning, distributed representation-based reasoning, neural network-based reasoning, and mixed reasoning. These methods are summarized in detail, and the future research directions and prospects of knowledge reasoning based on knowledge graphs are discussed and prospected.

Language: en

Keywords

distributed representation; knowledge graph; knowledge reasoning; neural network; rule reasoning