Extracting Related Concepts from Wikipedia by Using a Graph Database System

被引：0
|
作者：
Omasa, Asuka [1 ]
Inoue, Ushio [1 ]
机构：
[1] Tokyo Denki Univ, Grad Sch Engn, Tokyo, Japan
来源：
2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) | 2019年
关键词：
semantic relatedness; graph databases; Wikipedia; web structure mining;
D O I：
10.1109/snpd.2019.8935874
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Wikipedia has been used as a corpus for research on natural language processing due to a huge number of articles and a high comprehensiveness of its vocabulary. The purpose of this research is to measure the semantic relatedness between two concepts covered by Wikipedia articles. The hyperlink structure among articles is focused because a link from an article to another article indicates an explicit relationship between them. However, the cost of analyzing links may be a problem if the article links to or linked from a lot of articles. A graph database system enables quick and efficient access to related data without handling unrelated data, because it stores data with predefined relationships among data. This paper proposes a method for extracting related concepts from Wikipedia by using a graph database system.
引用
收藏
页码：268 / 273
页数：6
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