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Enabling Representation Learning in Ontology-Driven Conceptual Modeling Using Graph Neural Networks

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Advanced Information Systems Engineering (CAiSE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13901))

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Abstract

Conceptual Models (CMs) are essential for information systems engineering since they provide explicit and detailed representations of the subject domains at hand. Ontology-driven conceptual modeling (ODCM) languages provide primitives for articulating these domain notions based on the ontological categories put forth by upper-level (or foundational) ontologies. Many existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Connecting these models to ontological categories would provide better support for meaning negotiation, semantic interoperability, and complexity management. However, given the sheer size of this legacy base, manual stereotyping is a prohibitive task. This paper addresses this problem by proposing an approach based on Graph Neural Networks towards automating the task of stereotyping UML class diagrams with the meta-classes offered by the ODCM language OntoUML. Since these meta-classes (stereotypes) represent ontological distinctions put forth by a foundational ontology, this task is equivalent to ontological category prediction for these classes. To enable this approach, we propose a strategy for representing CM vector embeddings that preserve the model elements’ structure and ontological categorization. Finally, we present an evaluation that shows convincing learning of OntoUML model node embeddings used for OntoUML stereotype prediction.

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Notes

  1. 1.

    Note that the edges in Table 2 do not denote the OntoUML relations as they were transformed into CKG nodes to enable their prediction.

  2. 2.

    Deep Graph Library: https://www.dgl.ai/.

  3. 3.

    This function aggregates the information from a sample of node’s neighbors.

  4. 4.

    http://shorturl.at/EHKNT.

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Acknowledgements

This work has been partially funded through the Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) under the contract number 854187.

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Authors and Affiliations

  1. Business Informatics Group, TU Wien, Vienna, Austria

    Syed Juned Ali & Dominik Bork

  2. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

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  1. Syed Juned Ali
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  2. Giancarlo Guizzardi
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  3. Dominik Bork
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Correspondence to Syed Juned Ali .

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Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Marta Indulska

  2. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  3. Universidad San Jorge, Zaragoza, Spain

    Carlos Cetina

  4. Universitat Politècnica de València, Valencia, Spain

    Oscar Pastor

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Ali, S.J., Guizzardi, G., Bork, D. (2023). Enabling Representation Learning in Ontology-Driven Conceptual Modeling Using Graph Neural Networks. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds) Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science, vol 13901. Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-34560-9_17

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