Open AccessResearch on Key Technologies of Knowledge Graph Construction Based on Natural Language Processing
Author(s) -
Guilei Wang,
Tao Yue,
Haixu Ma,
Tong Bao,
Jingmiao Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032057
Subject(s) - computer science , knowledge graph , natural language processing , domain knowledge , graph , knowledge extraction , artificial intelligence , natural language , information extraction , information retrieval , theoretical computer science
As we all know, building a domain knowledge graph from a large amount of text requires a very large amount of work, including entity recognition, entity disambiguation, relationship extraction, and event extraction, etc. It is difficult to build a very comprehensive domain knowledge graph from scratch. Fortunately, with the rapid progress of natural language processing technology, we can use a large number of natural language processing tools to help us build a domain knowledge graph. This article mainly studies the extraction of domain terms in the process of constructing the knowledge graph. The natural language processing techniques used are mainly new word discovery, word segmentation, and keyword extraction. This paper improves the existing imperfect natural language processing technologies and applies them to the process of constructing the domain knowledge graph in order to construct the domain knowledge graph accurately and efficiently.