research-article

An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval

Authors:

Pablo Castells,

Miriam Fernandez,

David ValletAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 19, Issue 2

Pages 261 - 272

https://doi.org/10.1109/TKDE.2007.22

Published: 01 February 2007 Publication History

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.

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Pahuja VWang BLatapie HSrinivasa JSu YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)A Retrieve-and-Read Framework for Knowledge Graph Link PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614769(1992-2002)Online publication date: 21-Oct-2023
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Index Terms

An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 19, Issue 2

February 2007

197 pages

ISSN:1041-4347

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Copyright © 2007.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 February 2007

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Cited By

Pahuja VWang BLatapie HSrinivasa JSu YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)A Retrieve-and-Read Framework for Knowledge Graph Link PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614769(1992-2002)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614769
Vuong YBui QNguyen HNguyen TTran VPhan XSatoh KNguyen L(2022)SM-BERT-CR: a deep learning approach for case law retrieval with supporting modelArtificial Intelligence and Law10.1007/s10506-022-09319-631:3(601-628)Online publication date: 10-Aug-2022
https://dl.acm.org/doi/10.1007/s10506-022-09319-6
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https://dl.acm.org/doi/10.1007/s00158-022-03425-4
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Mustafi DSahoo G(2019)A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clusteringSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3289-423:15(6361-6378)Online publication date: 1-Aug-2019
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Saravanan KRadhakrishnan A(2018)Dynamic Search Engine Platform for Cloud Service Level Agreements Using Semantic AnnotationInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201807010414:3(70-98)Online publication date: 1-Jul-2018
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