Open AccessExplicit semantic path mining via wikipedia knowledge tree
Author(s) -
Xia Tian,
Chen Miao,
Liu Xiaozhong
Publication year - 2014
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.2014.14505101160
Subject(s) - computer science , information retrieval , path (computing) , viewpoints , ranking (information retrieval) , vector space model , explicit semantic analysis , knowledge extraction , word (group theory) , natural language processing , knowledge base , semantic computing , data mining , artificial intelligence , semantic technology , semantic web , mathematics , art , geometry , visual arts , programming language
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.