An adaptation of the vector-space model for ontology-based information retrieval

被引:217
作者
Castells, Pablo [1 ]
Fernandez, Miriam [1 ]
Vallet, David [1 ]
机构
[1] Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain
关键词
information retrieval models; ontology languages; semantic search; Semantic Web;
D O I
10.1109/TKDE.2007.22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:261 / 272
页数:12
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