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Information Processing and Management: an International Journal
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Inductive learning algorithms and representations for text categorization
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A language modeling approach to information retrieval
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Information Processing and Management: an International Journal
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Semantic Granularity in Ontology-Driven Geographic Information Systems
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ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
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Intelligent Encoding of Concepts in Web Document Retrieval
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Augmenting Naive Bayes Classifiers with Statistical Language Models
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CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
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IEEE Transactions on Knowledge and Data Engineering
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Using information content to evaluate semantic similarity in a taxonomy
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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
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Information Sciences: an International Journal
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Both similarity-based and popularity-based document ranking functions have been successfully applied to information retrieval (IR) in general. However, the dimension of semantic granularity also should be considered for effective retrieval. In this article, we propose a semantic granularity-based IR model that takes into account the three dimensions, namely similarity, popularity, and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a domain ontology. Semantic granularity refers to the levels of semantic detail carried by an information item. The results of our benchmark experiments confirm that the proposed semantic granularity based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. The perceived relevance of the documents delivered by the granularity-based IR system is significantly higher than that produced by a popular search engine for a number of domain-specific search tasks. To the best of our knowledge, this is the first study regarding the application of semantic granularity to enhance domain-specific IR.