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.
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Wu, X., Huang, S., Zhang, J., Yu, Y. (2006). Hierarchical Topic Term Extraction for Semantic Annotation in Chinese Bulletin Board System. In: Mizoguchi, R., Shi, Z., Giunchiglia, F. (eds) The Semantic Web – ASWC 2006. ASWC 2006. Lecture Notes in Computer Science, vol 4185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11836025_4
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DOI: https://doi.org/10.1007/11836025_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38329-1
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