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The Ontology for Conceptual Characterization of Ontologies

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Abstract

Ontologies as computational artifacts have been seen as a solution to FAIRness due to their characteristics, applications, and semantic competencies. Conceptualizations of complex and vast domains can be fragmented in different ways and can compose what is known as ontology networks. Thus, the ontologies produced can relate to each other in many different ways, making the ontological artifacts themselves subject to FAIRness. The problem is that in the Ontology Engineering Process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. Besides, usually, Ontology Engineers provide well-grounded reference ontologies, but rarely are they implemented. At the same time, Domain Specialists produce operational ontologies storing large amounts of valid data but with naive ontological support or even without any. We address this problem of lack of consensual conceptualization by proposing a reference conceptual model (O4OA) that considers ontological-related and domain-related perspectives, knowledge, and commitment necessary to facilitate the process of Ontological Analysis, including the analysis of ontologies composing an ontology network. Indeed, O4OA is a (meta)ontology grounded in the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR principles. We demonstrate how this approach can suitably promote conceptual clarification and terminological harmonization in this area through our framework proposal and its case studies.

The authors are grateful to the members of the PROS Center Genome group and Ontology from UPV and Conceptual Modeling Research Group (NEMO) from UFES for fruitful discussions.

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Notes

  1. 1.
  2. 2.

    Readers may find the complete description of O4OA competence questions at the following repository:https://bfmartins.gitlab.io/o4oa/.

  3. 3.

    Part of the elicitation process happened during the COVID-2019 pandemic, so the remote strategy was mandatory.

  4. 4.

    Our research is part of a research consortium to develop well-grounded knowledge graphs through a comprehensive solution within a project in collaboration with teams from several academic institutions, and Accenture LTD.

  5. 5.

    Which is more general than Domain, Task, and Application ontologies, but more specific than Foundational Ontologies.

  6. 6.
  7. 7.

    The adoption of microservices allows API scaling adding reasoning capabilities, for this future possibilities.

  8. 8.

    It is important to point out that we adopted anAgile Development approach in order to provide fast initial results meanwhile being scaled.

  9. 9.

    SSN, oneM2M, and SAREF areCore Ontologies in the sense of [22,65].

  10. 10.

    In O4OA, the relations and concepts of ontologies are data instances.

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Acknowledgments

This work has financial support of Accenture LTD with project Digital Knowledge Graph – Adaptable Analytics API, Generalitat Valenciana with the project CoMoDiD (CIPROM/2021/023), Spanish State Research Agency with projects DELFOS (PDC2021-121243-I00), SREC (PID2021-123824OB-I00), MICIN/AEI/10.13039/501 100011033, and co-financed by the European Union Next Generation EU/PRTR with ERDF.

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Authors and Affiliations

  1. Universitat Politècnica de València, VRAIN, Camino de Vera, 46022, Valencia, Spain

    Beatriz Franco Martins, José Fabián Reyes Román & Oscar Pastor

  2. University of Twente, Twente, The Netherlands

    Renata Guizzardi

  3. Accenture LTD, Cyber R&D Lab, Ha-Menofim St 2, 4672553, Tel Aviv-Yafo, Israel

    Moshe Hadad

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  2. Renata Guizzardi

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  3. José Fabián Reyes Román

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  4. Moshe Hadad

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Correspondence toBeatriz Franco Martins orOscar Pastor.

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  1. Federal University of Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  2. Universidade de Lisboa, Lisbon, Portugal

    José Borbinha

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. University of Auckland, Auckland, New Zealand

    Sebastian Link

  5. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

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Martins, B.F., Guizzardi, R., Román, J.F.R., Hadad, M., Pastor, O. (2023). The Ontology for Conceptual Characterization of Ontologies. In: Almeida, J.P.A., Borbinha, J., Guizzardi, G., Link, S., Zdravkovic, J. (eds) Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14320. Springer, Cham. https://doi.org/10.1007/978-3-031-47262-6_6

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