Towards AI Assisted Domain Modeling

Auteurs

Feltus C., Ma Q., Proper H.A., Kelsen P.

Référence

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13012 LNCS, pp. 75-89, 2021

Description

A domain model provides an explicit knowledge representation of (selected aspects of) some domain of interest. The transition to the digital age results in an increased need for domain models that are machine understandable. We posit that, at the same time, there is an increasing need for non-experts (in modeling) to be able to create such models, or at least be able to understand the created models, and take ownership of their meaning and implications. This situation causes a ‘modeling bottleneck’ in that it is not reasonable to expect all non-experts to become modeling experts. This is where we turn to AI as an enabling technology to support non-experts in domain modeling related tasks; i.e. AI Assisted Domain Modeling. We foresee a symbiotic collaboration between human intelligence, symbolic AI and subsymbolic AI; essentially resulting in a triple-helix of human, symbolic, and subsymbolic intelligence. The aim of this workshop paper is to structurally explore the potential role of (symbolic and subsymbolic) AI to support domain conceptualization. To do so, we will combine three perspectives on domain modeling: (1) a framework relating the different conceptions (harbored in the mind of a modeler) regarding the domain to be modeled, and the model itself, (2) the role of normative frames towards modeling activities, and (3) modeling as a structured dialogue between an (automated) system analyst and a domain expert.

Lien

doi:10.1007/978-3-030-88358-4_7