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Machine Learning Selection of Candidate Ontologies for Automatic Extraction of Context Words and Axioms from Ontology Corpus

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Information Integration and Web Intelligence (iiWAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13635))

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Abstract

Ontology selection for reuse is a challenging task an ontology/knowledge engineer can face due to the diversity and abundance of ontologies on the internet today. This has become an active research area in ontology engineering. In this study, candidate ontologies are selected from a corpus using Skip-gram, a neural networks algorithm. The word embeddings of the vocabulary of 12 ontologies in the corpus were used to train the Skip-gram models. Thereafter, concepts from a text corpus were utilized to test the models and select the candidate ontologies for context words and axioms extraction. The results showed that the proposed method can automatically extract context words, and the URIs and labels of different types of axioms from the candidate ontologies. This gives the knowledge/ontology engineers detailed information about the contents of candidate ontologies so that they can make an informed decision on the parts of the ontologies they would like to reuse.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of Kwazulu-Natal, Pietermaritzburg, South Africa

    Mohammed Suleiman Mohammed Rudwan & Jean Vincent Fonou-Dombeu

Authors
  1. Mohammed Suleiman Mohammed Rudwan
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  2. Jean Vincent Fonou-Dombeu
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Corresponding author

Correspondence to Mohammed Suleiman Mohammed Rudwan .

Editor information

Editors and Affiliations

  1. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  2. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  3. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

  4. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

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Rudwan, M.S.M., Fonou-Dombeu, J.V. (2022). Machine Learning Selection of Candidate Ontologies for Automatic Extraction of Context Words and Axioms from Ontology Corpus. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-21047-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21046-4

  • Online ISBN: 978-3-031-21047-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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