Toward a semantic granularity model for domain-specific information retrieval

Yan, Xin, Lau, Raymond Y.K., Song, Dawei, Li, Xue and Ma, Jian (2011) Toward a semantic granularity model for domain-specific information retrieval. Acm Transactions On Information Systems, 29 3: . doi:10.1145/1993036.1993039

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Author Yan, Xin
Lau, Raymond Y.K.
Song, Dawei
Li, Xue
Ma, Jian
Title Toward a semantic granularity model for domain-specific information retrieval
Journal name Acm Transactions On Information Systems   Check publisher's open access policy
ISSN 1046-8188
Publication date 2011-07
Sub-type Article (original research)
DOI 10.1145/1993036.1993039
Open Access Status
Volume 29
Issue 3
Total pages 46
Place of publication New York, NY, United States
Publisher Association for Computing Machinery
Collection year 2012
Language eng
Abstract 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.
Keyword Theory
Document ranking
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 29 times in Scopus Article | Citations
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