Semantic search has been one of the motivations of the Semantic Web since it was envisioned. In my thesis I research the development of a new retrieval model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In this 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, my current 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. The method has been tested on corpora of significant size, showing promising results respect to keyword-based search, and providing ground for further analysis and research.

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