Abstract
This paper describes a method of extracting a large set of hyponymy relations with a high precision from hierarchical layouts in Wikipedia articles. Hyponymy relation has been studied as one of the principal knowledge for information retrieval and web directory, which helps users to access the growing web. Various methods have been proposed to automatically acquire hyponymy relations. In this article, we first extract hyponymy relation candidates from sections and itemizations in hierarchical layouts of Wikipedia articles, and then filter out irrelevant candidates by using a machine learning technique. In experiments, we successfully extracted more than 1.35 million relations from the hierarchical layouts in the Japanese version of Wikipedia, with a precision of 90%.
