Conceptual Modeling and Artificial Intelligence: A Systematic Mapping Study
This work addresses the integration of human-interpretable knowledge representations from CM with AI's data-driven pattern extraction, which is incremental as it synthesizes existing research rather than proposing new methods.
The study systematically maps the interdisciplinary research field of conceptual modeling (CM) and artificial intelligence (AI), identifying how their intertwining provides mutual benefits and outlining future research directions.
In conceptual modeling (CM), humans apply abstraction to represent excerpts of reality for means of understanding and communication, and processing by machines. Artificial Intelligence (AI) is applied to vast amounts of data to automatically identify patterns or classify entities. While CM produces comprehensible and explicit knowledge representations, the outcome of AI algorithms often lacks these qualities while being able to extract knowledge from large and unstructured representations. Recently, a trend toward intertwining CM and AI emerged. This systematic mapping study shows how this interdisciplinary research field is structured, which mutual benefits are gained by the intertwining, and future research directions.