IBM Research, Brazil E-mail: raphaelt@br.ibm.com"/> Institute for Data, Process and Knowledge Management, Vienna University of Economics and Business (WU), Vienna, Austria E-mail: kate.revoredo@wu.ac.at"/> Institute of Computing, Universidade Federal Fluminense (UFF), Niteroi, RJ, Brazil E-mail: alinepaes@ic.uff.br"/>
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2021 Volume 36
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RESEARCH ARTICLE   Open Access    

Learning multiple concepts in description logic through three perspectives

  • IBM Research, Brazil E-mail: raphaelt@br.ibm.com

  • Institute for Data, Process and Knowledge Management, Vienna University of Economics and Business (WU), Vienna, Austria E-mail: kate.revoredo@wu.ac.at

  • Institute of Computing, Universidade Federal Fluminense (UFF), Niteroi, RJ, Brazil E-mail: alinepaes@ic.uff.br

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  • Abstract: An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.
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  • Cite this article

    Raphael Melo, Kate Revoredo, Aline Paes. 2021. Learning multiple concepts in description logic through three perspectives. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000059
    Raphael Melo, Kate Revoredo, Aline Paes. 2021. Learning multiple concepts in description logic through three perspectives. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000059
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RESEARCH ARTICLE   Open Access    

Learning multiple concepts in description logic through three perspectives

  • IBM Research, Brazil E-mail: raphaelt@br.ibm.com

  • Institute for Data, Process and Knowledge Management, Vienna University of Economics and Business (WU), Vienna, Austria E-mail: kate.revoredo@wu.ac.at

  • Institute of Computing, Universidade Federal Fluminense (UFF), Niteroi, RJ, Brazil E-mail: alinepaes@ic.uff.br

  • Received: 30 December 2019
  • Revised: 09 April 2021
  • Accepted: 20 April 2021
  • Published online: 19 May 2021
The Knowledge Engineering Review  36,  2021, 36(1)  |  Cite this article

Abstract: Abstract: An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

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    Acknowledgments

    • The first author would like to thank CAPES for the financial support through a master scholarship. The second author was partially supported by Capes-DAAD, and the third author would like to thank the Brazilian research agencies CNPq and FAPERJ for the financial support.

    • The depth of a theory is the maximum number of inferences required to answer a goal query.

    • This is a general principle on Machine Learning: The amount of relevant information positively affects the quality of learned models.

    • The definition for GP makes a cycle.

    • Depth is the number of inferential steps taken until a cycle is found.

    • http://oaei.ontologymatching.org/.

    • http://dl-learner.org/Projects/DLLearner.

    • DL-Learner was used.

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    • © The Author(s), 2021. Published by Cambridge University Press2021Cambridge University Press
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    Raphael Melo, Kate Revoredo, Aline Paes. 2021. Learning multiple concepts in description logic through three perspectives. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000059
    Raphael Melo, Kate Revoredo, Aline Paes. 2021. Learning multiple concepts in description logic through three perspectives. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000059
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