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Fuzzy Ontology-Based Possibilistic Approach for Document Indexing Using Semantic Concept Relations

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Database and Expert Systems Applications (DEXA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12924))

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Abstract

To overcome the weaknesses of current information retrieval system and to utilize the strengths knowledge extraction a novel approach based on fuzzy ontology and possibility theory is proposed for indexing documents. Possibility theory allows to model and quantify the relevance of a document given a controlled vocabulary through two measures: necessity and possibility. Fuzzy ontology is used to improve the indexing process on information retrieval by means of external resource. Besides, the fuzzy approach has been proposed in order to make the availability of terms representations in a document more flexible. It allows a formal representation of a knowledge domain in the form of a hierarchical terminology provided with semantic relationships. As a result, the proposed approach has been made on different corpora, had better performance than other indexing approaches and it prove important results.

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Notes

  1. 1.

    http://trec.nist.gov/data/t9_filtering.html.

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Author information

Authors and Affiliations

  1. MARS Research Laboratory, LR17ES05, University of Sousse, Sousse, Tunisia

    Kabil Boukhari & Mohamed Nazih Omri

Authors
  1. Kabil Boukhari
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  2. Mohamed Nazih Omri
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Corresponding author

Correspondence to Mohamed Nazih Omri .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. Johannes Kepler University Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  3. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  4. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Boukhari, K., Omri, M.N. (2021). Fuzzy Ontology-Based Possibilistic Approach for Document Indexing Using Semantic Concept Relations. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12924. Springer, Cham. https://doi.org/10.1007/978-3-030-86475-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-86475-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86474-3

  • Online ISBN: 978-3-030-86475-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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