An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
Pablo Castells; Miriam Fernandez; David Vallet
Knowledge and Data Engineering, IEEE Transactions on
Volume 19, Issue 2, Feb. 2007 Page(s):261 - 272
Digital Object Identifier 10.1109/TKDE.2007.22
Summary:Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
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