A query-aware document ranking method for geographic information retrieval
Proceedings of the 4th ACM workshop on Geographical information retrieval
Learning Fuzzy Models of User Interests in a Semantic Information Retrieval System
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Exploiting location information for Web search
Computers in Human Behavior
An approach based on langage modeling for improving biomedical information retrieval
International Journal of Knowledge-based and Intelligent Engineering Systems
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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.