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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.
Information retrieval models, ontology languages, semantic search, semantic Web.

P. Castells, D. Vallet and M. Fernández, "An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval," in IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 261-272, 2007.
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