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Improving Conceptual Domain Characterization in Ontology Networks

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Research Challenges in Information Science: Information Science and the Connected World (RCIS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 476))

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Abstract

The community of Conceptual Modeling (CM) perception that Semantic Interoperability cannot be achieved without the support of an ontology-driven approach has become increasingly consensual. Moreover, the more complex and extensive the domain of the application of conceptual models, the harder it is to achieve semantic consensus. Therefore, it has emerged the perception that ontologies built to describe complex domains should not be overly large or be used in isolation. Ontology Networks arose to cover this issue. The community had to deal with issues such as different ontologies of the network using the same concept with different meanings or the same term used to designate distinct concepts. We developed a framework for classifying ontologies that provides a stable and homogeneous environment to facilitate the ontological analysis process by dealing simultaneously with ontological and domain perspectives. This article presents our proposal where conceptualization is used to identify the relationships among the evaluated ontologies. Our goal is to facilitate semantic consensus, providing guidelines and best practices supported by a stable, homogeneous, and repeatable environment.

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Notes

  1. 1.

    https://www.iso.org/standard/71952.html.

  2. 2.

    https://www.iso.org/standard/63712.html.

  3. 3.

    https://www.iso.org/standard/73906.html.

  4. 4.

    https://www.iso.org/standard/44375.html.

  5. 5.

    https://www.w3.org/TR/dwbp/.

  6. 6.

    “Functional requirements refer to the knowledge to be represented” [1].

  7. 7.

    Foundational Ontologies express very general concepts and their relations.

  8. 8.

    Domain Ontologies conceptualize for specific domains.

  9. 9.

    Task Ontologies conceptualize of domain tasks, processes, and activities.

  10. 10.

    Application Ontologies encompass both contexts of Domain and Task Ontologies.

  11. 11.

    Core Ontologies are more general than the other Non-Foundational Ontologies, but more specific than Foundational Ontologies.

  12. 12.

    Given the ontological notion adopted in UFO [21].

  13. 13.

    http://bron.alfa.csail.mit.edu/info.html.

  14. 14.

    http://alfagroup.csail.mit.edu/.

  15. 15.

    https://github.com/ALFA-group/BRON.

  16. 16.

    https://attack.mitre.org/.

  17. 17.

    https://capec.mitre.org/index.html.

  18. 18.

    https://github.com/mitre/engage.

  19. 19.

    https://cwe.mitre.org/.

  20. 20.

    https://www.cve.org/.

  21. 21.

    Our research is part of a project to develop KGs (TKG and DTKGs) through a comprehensive solution within a project with Accenture LTD. The consortium also has research in partnership with other academic research centers.

  22. 22.

    For example, see https://www.cvedetails.com/cve/CVE-1999-0067/. Note that CWE is not defined for this vulnerability in this example.

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Acknowledgements

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

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Authors and Affiliations

  1. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Beatriz Franco Martins, José Fabián Reyes Román & Oscar Pastor

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

    Moshe Hadad

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  1. Beatriz Franco Martins
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Correspondence to Beatriz Franco Martins or Oscar Pastor .

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Editors and Affiliations

  1. Université Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  2. University of Bergen, Bergen, Norway

    Andreas L. Opdahl

  3. University of Essex, Colchester, UK

    Haralambos Mouratidis

  4. Ionian University, Corfu, Greece

    Aggeliki Tsohou

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Martins, B.F., Román, J.F.R., Pastor, O., Hadad, M. (2023). Improving Conceptual Domain Characterization in Ontology Networks. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-031-33080-3_12

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