Explicit semantic path mining via wikipedia knowledge tree

Abstract

While classical bag-of-word (BoG) approaches represent text content in the word level, recent studies show that knowledge-based concept indexation is a promising approach to further enhance the text search and mining performance. In this study, we propose a new knowledge indexation/extraction method, Explicit Semantic Path Mining (ESPM), for knowledge-base text mining. It has roots in a concept-based vector constructing method, Explicit Semantic Analysis (ESA), which has shown success in text mining tasks. For this new method, given an input piece of text, ESPM can efficiently identify the independent and optimized semantic path(s) on a concept map, which is, in this study, the Wikipedia category tree. Unlike earlier studies focusing on BoG based vector space, ESPM is a semantic path mining algorithm, which generates the top down semantic categories of a given text by leveraging the rich link information between Wikipedia categories and articles. Preliminary experiment based on ODP data shows ESPM delivers high quality independent semantic paths from both precision and ranking viewpoints.