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Modern Information Retrieval
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Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
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PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
MENTA: inducing multilingual taxonomies from wikipedia
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Web Intelligence and Agent Systems
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In this paper, we address a novel method of Web query expansion by using WordNet and TSN. WordNet is an online lexical dictionary which describes word relationships in three dimensions of Hypernym, Hyponym and Synonym. And their impacts to expansions are different. We provide quantitative descriptions of the query expansion impact along each dimension. However, WordNet may bring many noises for the expansion due to its collection independent characteristic. Furthermore, it may not catch current state of words and their relationships because of the explosive increase of the Web. To overcome those problems, collection-based TSN (Term Semantic Network) is created with respect to word co-occurrence in the collection. We use TSN both as a filter and a supplement for WordNet. We also provide a quantitatively study as what is the best way for the expansion with TSN. In our system, we combine the query expansions along each semantic dimension as our overall solution. Our experiments reveal that the combined expansion can provide a satisfied result for the Web query performance. The methodologies in this paper have been already employed in our Web image search engine system.