Evidence-based lean logic profiles for conceptual data modelling languages

Fillottrani, Pablo R and Keet, C. Maria (2018) Evidence-based lean logic profiles for conceptual data modelling languages, CS18-03-00, Department of Computer Science, University of Cape Town.

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Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.

Item Type: Technical report
Uncontrolled Keywords: Conceptual modeling, description logics
Subjects: Computing methodologies > Artificial intelligence
Alternate Locations: https://arxiv.org/abs/1809.03001
Date Deposited: 09 Nov 2018
Last Modified: 10 Oct 2019 15:31
URI: http://pubs.cs.uct.ac.za/id/eprint/1278

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