Yan, Xin; Lau, Raymond Y. K.; Song, Dawei; Li, Xue and Ma, Jian
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1145/1993036.1993039|
|Google Scholar:||Look up in Google Scholar|
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.
|Item Type:||Journal Article|
|Copyright Holders:||2011 ACM|
|Keywords:||document ranking; domain-specific search; domain ontology; information retrieval; granular computing|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
|Depositing User:||Dawei Song|
|Date Deposited:||21 Jun 2012 08:49|
|Last Modified:||24 Feb 2016 12:37|
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