Research on knowledge extraction in knowledge graph construction

Abstract

Knowledge Graphs (KGs) are composed of structured information in the form of entities and relations. And the process of extracting entities and relations from data is called Knowledge Extraction. Knowledge extraction is a fundamental task in the field of Natural Language Processing (NLP) and a key part of knowledge graph construction. In this paper, we provide comprehensive research on knowledge extraction in knowledge graph construction. We first introduce the technical architecture of the KGs and the classification of knowledge extraction. Then, we systematically categorize existing works based on the development of knowledge extraction. Finally, we review current open-source tools for knowledge extraction and summarize their advantages and disadvantages.

A Knowledge Graph Data Expansion Method Based on Relational Propensity Categories with Legal Applications

“…Search engines such as Google and Bing are using knowledge graphs to enhance their search experience, and social networking applications such as Facebook are using it to mine connections between users. Knowledge graph research also includes, but is not limited to, semantic parsing, entity alignment, information extraction, relationship extraction, entity linking, and *Corresponding author's email: mengshi@zjgsu.edu.cn various other tasks [2][3][4]. In addition to these, Knowledge 1 Graphs have been widely used in practical applications in a variety of fields such as intelligent search, recommender systems, deep Q&A, natural language processing, and so on.…”