research-article

Predicting the possibilistic score of OWL axioms through modified support vector clustering

Authors:

Dario Malchiodi,

Andrea G. B. TettamanziAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 1984 - 1991

https://doi.org/10.1145/3167132.3167345

Published: 09 April 2018 Publication History

Abstract

We address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic considerations and reminiscent of the Jaccard index. We show that the proposed method successfully learns the possibilistic score in a knowledge base consisting of pairs of candidate OWL axioms, meanwhile highlighting that a small subset of the considered axioms turns out harder to learn than the remainder.

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Cited By

Ballout Ada Costa Pereira CTettamanzi A(2023)Predicting the Acceptability of Atomic Candidate OWL Class Axioms2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00055(339-345)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00055
Ballout ATettamanzi ADa Costa Pereira C(2022)Predicting the Score of Atomic Candidate OWL Class Axioms2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00020(72-79)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00020
Malchiodi Dda Costa Pereira CTettamanzi A(2020)Classifying Candidate Axioms via Dimensionality Reduction TechniquesModeling Decisions for Artificial Intelligence10.1007/978-3-030-57524-3_15(179-191)Online publication date: 2-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-57524-3_15
Show More Cited By

Index Terms

Predicting the possibilistic score of OWL axioms through modified support vector clustering
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Ontology engineering
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
      1. Kernel methods
2. Information systems
  1. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Web Ontology Language (OWL)

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cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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New York, NY, United States

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Published: 09 April 2018

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SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Ballout Ada Costa Pereira CTettamanzi A(2023)Predicting the Acceptability of Atomic Candidate OWL Class Axioms2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00055(339-345)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00055
Ballout ATettamanzi ADa Costa Pereira C(2022)Predicting the Score of Atomic Candidate OWL Class Axioms2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00020(72-79)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00020
Malchiodi Dda Costa Pereira CTettamanzi A(2020)Classifying Candidate Axioms via Dimensionality Reduction TechniquesModeling Decisions for Artificial Intelligence10.1007/978-3-030-57524-3_15(179-191)Online publication date: 2-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-57524-3_15
Cermenati LMalchiodi DZanaboni A(2019)Simultaneous Learning of Fuzzy SetsNeural Approaches to Dynamics of Signal Exchanges10.1007/978-981-13-8950-4_16(167-175)Online publication date: 19-Sep-2019
https://doi.org/10.1007/978-981-13-8950-4_16
Malchiodi DZanaboni A(2019)Data-Driven Induction of Shadowed Sets Based on Grade of FuzzinessFuzzy Logic and Applications10.1007/978-3-030-12544-8_2(17-28)Online publication date: 23-Feb-2019
https://doi.org/10.1007/978-3-030-12544-8_2
Malchiodi Dda Costa Pereira CTettamanzi A(2018)Predicting the Possibilistic Score of OWL Axioms Through Support Vector RegressionScalable Uncertainty Management10.1007/978-3-030-00461-3_28(380-386)Online publication date: 11-Sep-2018
https://doi.org/10.1007/978-3-030-00461-3_28

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