Fuzzy Ontology Mining and Semantic Information Granulation for Effective Information Retrieval Decision Making

DOI

10.2991/ijcis.2011.4.1.5How to use a DOI?

Keywords

Text Mining, Fuzzy Ontology, Fuzzy Subsumption, Information Granulation, Granular Computing, Information Retrieval.

Abstract

The notion of semantic information granulation is explored to estimate the information specificity or generality of documents. Basically, a document is considered more specific than another document if it contains more cohesive domain-specific terminologies than that of the other one. We believe that the dimension of semantic granularity is an important supplement to the existing similarity-based and popularity-based measures for building effective document ranking functions. The main contributions of this paper is the illustration of the design and development of a fuzzy ontology based granular information retrieval (IR) system to improve the effectiveness of IR decision making for various domains. Based on the notion of semantic information granulation, a novel computational model is developed to estimate the semantic granularity of documents; these documents can then be ranked according to the information seekers' specific semantic granularity requirements. One main component of the proposed computational model is the fuzzy ontology mining mechanism which can automatically build domain-specific ontology for the estimation of semantic granularity of documents. Our TREC-based experiment reveals that the proposed fuzzy ontology based granular IR system outperforms a classical vector space based IR system in domain specific IR. Our research work opens the door to the applications of granular computing and fuzzy ontology mining methods to enhance domain specific IR decision making.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Chapmann C.L. Lai
AU  - Raymond Y.K. Lau
AU  - Yuefeng Li
PY  - 2011
DA  - 2011/02/01
TI  - Fuzzy Ontology Mining and Semantic Information Granulation for Effective Information Retrieval Decision Making
JO  - International Journal of Computational Intelligence Systems
SP  - 54
EP  - 65
VL  - 4
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.1.5
DO  - 10.2991/ijcis.2011.4.1.5
ID  - Lai2011
ER  -