DocumentCode
2530787
Title
Concept-based term weighting for Web information retrieval
Author
Zakos, John ; Verma, Brijesh
Author_Institution
Sch. of Inf. & Commun. Technol., Griffith Univ., Brisbane, Qld., Australia
fYear
2005
fDate
16-18 Aug. 2005
Firstpage
173
Lastpage
178
Abstract
In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for Web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed the significance of this technique is that 1) unlike IDF, CBW is independent of document collection statistics, 2) it presents a new way of interpreting ontologies for retrieval and 3) it introduces an additional source of term importance information that can be used for term weighting.
Keywords
Internet; information retrieval; ontologies (artificial intelligence); vocabulary; CBW technique; Web information retrieval; concept-based term weighting; document collection statistics; inverse document frequency; ontology; term importance determination; Australia; Computational intelligence; Degradation; Equations; Frequency measurement; Information retrieval; Information technology; Ontologies; Statistics;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Multimedia Applications, 2005. Sixth International Conference on
Print_ISBN
0-7695-2358-7
Type
conf
DOI
10.1109/ICCIMA.2005.20
Filename
1540721
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