Abstract
Conceptual modeling plays a fundamental role to capture information about complex business domains (e.g., finance, healthcare) and enables semantic interoperability. To fulfill their role, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, modelers are subject to cognitive limitations and biases and, hence, in practice, they produce models that fall short in that respect. Moreover, the process of formally designing conceptual models is notoriously hard and requires expertise that modelers do not always have. This paper falls in the general area concerned with the development of artificial intelligence techniques for the enterprise. In particular, we propose an approach that leverages model finding and inductive logic programming (ILP) techniques. We aim to move towards supporting modelers in identifying domain constraints that are missing from their models, and thus improving their precision w.r.t. their intended worldviews. Firstly, we describe how to use the results produced by the application of model finding as input to an inductive learning process. Secondly, we test the approach with the goal of demonstrating its feasibility and illustrating some key design issues to be considered while using these techniques.
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Notes
- 1.
- 2.
In OntoUML, all kinds are mutually disjoint [15].
- 3.
Currently, the logical layer can be encoded by First Order Logic FOL syntax or by Description Logic (DL) syntax, covering ALC, SHOIQ, and SROIQ expressivity.
- 4.
All data used for the case study described in this section are available for research purposes at https://github.com/unibz-core/Mind-the-Gap.
- 5.
A litmus test is “a critical indicator of future success or failure”. A is a litmus test for B if A can be effectively used to measure some property of B [5].
- 6.
Notice that the output provided by the applied algorithm can be taken as a rule composed by axioms encoded in Description Logic (DL) or manchester owl syntax (www.w3.org/owl2-manchester-syntax/), and in order to map the output into FOL language, a further map** must be applied. For instance, the output resulting from the conjunction of the first three axioms provided as solution by the algorithm applied for the rule (1) above was:
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Fumagalli, M., Sales, T.P., Guizzardi, G. (2021). Mind the Gap!: Learning Missing Constraints from Annotated Conceptual Model Simulations. In: Serral, E., Stirna, J., Ralyté, J., Grabis, J. (eds) The Practice of Enterprise Modeling. PoEM 2021. Lecture Notes in Business Information Processing, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-030-91279-6_5
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