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Extracting Concepts' Relations and Users' Preferences for Personalizing Query Disambiguation

Extracting Concepts' Relations and Users' Preferences for Personalizing Query Disambiguation

Yan Chen, Yan-Qing Zhang
Copyright: © 2009 |Volume: 5 |Issue: 1 |Pages: 15
ISSN: 1552-6283|EISSN: 1552-6291|ISSN: 1552-6283|EISBN13: 9781615204823|EISSN: 1552-6291|DOI: 10.4018/jswis.2009010103
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MLA

Chen, Yan, and Yan-Qing Zhang. "Extracting Concepts' Relations and Users' Preferences for Personalizing Query Disambiguation." IJSWIS vol.5, no.1 2009: pp.65-79. http://doi.org/10.4018/jswis.2009010103

APA

Chen, Y. & Zhang, Y. (2009). Extracting Concepts' Relations and Users' Preferences for Personalizing Query Disambiguation. International Journal on Semantic Web and Information Systems (IJSWIS), 5(1), 65-79. http://doi.org/10.4018/jswis.2009010103

Chicago

Chen, Yan, and Yan-Qing Zhang. "Extracting Concepts' Relations and Users' Preferences for Personalizing Query Disambiguation," International Journal on Semantic Web and Information Systems (IJSWIS) 5, no.1: 65-79. http://doi.org/10.4018/jswis.2009010103

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Abstract

For most Web searching applications, queries are commonly ambiguous because words usually contain several meanings. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge models to find the most appropriate sense for the ambiguous word. Since queries are usually short, the contexts of the queries may not always provide enough information for disambiguating queries. Thus, more than one interpretation may be found for one ambiguous query. In this paper, we propose a cluster-based WSD method, which finds out all appropriate interpretations for the query. Because some senses of one ambiguous word usually have very close semantic relations, we group those similar senses together for explaining the ambiguous word in one interpretation. If the cluster-based WSD method generates several contradictory interpretations for one ambiguous query, we extract users’ preferences from clickthrough data, and determine suitable concepts or concepts’ clusters that meet users’ interests for explaining the ambiguous query.

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