Hierarchical topic term extraction for semantic annotation in Chinese bulletin board system

Abstract

With the current growing interest in the Semantic Web, the demand for ontological data has been on the verge of emergency. Currently many structured and semi-structured documents have been applied for ontology learning and annotation. However, most of the electronic documents on the web are plain-text, and these texts are still not well utilized for the Semantic Web. In this paper, we propose a novel method to automatically extract topic terms to generate a concept hierarchy from the data of Chinese Bulletin Board System (BBS), which is a collection of plain-text. In addition, our work provides the text source associated with the extracted concept as well, which could be a perfect fit for the semantic search application that makes a fusion of both formal and implicit semantics. The experimental results indicate that our method is effective and the extracted concept hierarchy is meaningful. © Springer-Verlag Berlin Heidelberg 2006.