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
%0 Journal Article
%1 castells_adaptation_2007
%A Castells, P
%A Fernandez, M
%A Vallet, D
%D 2007
%J Knowledge and Data Engineering, IEEE Transactions on
%K ontologien information_retrieval
%N 2
%P 261--272
%R 10.1109/tkde.2007.22
%T An adaptation of the vector-space model for ontology-based information retrieval
%U http://dx.doi.org/10.1109/tkde.2007.22
%V 19
%X 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
@article{castells_adaptation_2007,
abstract = {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},
added-at = {2018-11-04T17:00:37.000+0100},
author = {Castells, P and Fernandez, M and Vallet, D},
biburl = {https://www.bibsonomy.org/bibtex/22a5193077dbe6405ab74010c75bb03ad/lepsky},
doi = {10.1109/tkde.2007.22},
interhash = {45ed1e91a3c32d82055e938f8d323d30},
intrahash = {2a5193077dbe6405ab74010c75bb03ad},
issn = {1041-4347},
journal = {Knowledge and Data Engineering, IEEE Transactions on},
keywords = {ontologien information_retrieval},
number = 2,
pages = {261--272},
timestamp = {2018-11-07T09:14:29.000+0100},
title = {An adaptation of the vector-space model for ontology-based information retrieval},
url = {http://dx.doi.org/10.1109/tkde.2007.22},
volume = 19,
year = 2007
}