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Automatically Extracting OWL Versions of FOL Ontologies

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The Semantic Web – ISWC 2021 (ISWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12922))

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Abstract

While OWL and RDF are by far the most popular logic-based languages for Semantic Web Ontologies, some well-designed ontologies are only available in languages with a much richer expressivity, such as first-order logic (FOL) or the ISO standard Common Logic. This inhibits reuse of these ontologies by the wider Semantic Web Community. While converting OWL ontologies to FOL is straightforward, the reverse problem of finding the closest OWL approximation of an FOL ontology is undecidable. However, for most practical purposes, a “good enough” OWL approximation need not be perfect to enable wider reuse by the Semantic Web Community.

This paper outlines such a conversion approach by first normalizing FOL sentences into a function-free prenex conjunctive normal (FF-PCNF) that strips away minor syntactic differences and then applying a pattern-based approach to identify common OWL axioms. It is tested on the over 2,000 FOL ontologies from the Common Logic Ontology Repository.

This material is based in part upon work supported by The National Science Foundation under grants OIA-1937099, OIA-2033607, and III-1565811.

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Notes

  1. 1.

    The last sentence is not logically equivalent but still contains the same subclass relationship as one direction of the biconditional.

  2. 2.

    Data Properties are indistinguishable from Object Properties in FOL and not used.

  3. 3.

    Their exact FOL encoding does not really matter after the normalization step.

  4. 4.

    All Individuals encountered during parsing are declared as such in the OWL output.

  5. 5.

    http://colore.oor.net/bipartite_incidence/owl/interval_incidence.all.owl.

  6. 6.

    https://github.com/stain/profilechecker.

  7. 7.

    https://github.com/thahmann/macleod.

  8. 8.

    PLY is a Python port of the standard Unix tools Lex and Yacc.

  9. 9.

    https://docs.python.org/3/library/xml.etree.elementtree.html; the Owlready2 module was another option but writing axioms was not as straightforward.

  10. 10.

    For the gwml2 and the simple_features we only work with the complete ontologies because the submodules are not particularly meaningful on their own.

  11. 11.

    Full results are available from https://github.com/thahmann/macleod/blob/master/research/ISWC2021-experimental-data.xlsx and the OWL2 outputs are provided in https://colore.oor.net/ in the owl subfolder of each ontology hierarchy.

  12. 12.

    Recall that universally quantified conjunctions are split into separate sentences.

  13. 13.

    The 514 ontologies in [7] contain 618,260 classes but only 22,046 properties.

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Acknowledgment

We thank the four anonymous reviewers for their comments and suggestions that helped improve the final version.

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Authors and Affiliations

  1. School of Computing and Information Sciences, University of Maine, Orono, ME, 04469, USA

    Torsten Hahmann & Robert W. Powell II

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  1. Torsten Hahmann
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Correspondence to Torsten Hahmann .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. University of Düsseldorf, Düsseldorf, Germany

    Stefan Dietze

  4. IBM Research - Thomas J. Watson Research, Hawthorne, CA, USA

    Achille Fokoue

  5. University of Texas, Austin, TX, USA

    Ying Ding

  6. Imperial College, London, UK

    Payam Barnaghi

  7. Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  9. The Open University Walton Hall, Milton Keynes, UK

    Harith Alani

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Hahmann, T., Powell II, R.W. (2021). Automatically Extracting OWL Versions of FOL Ontologies. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-88361-4_15

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