Conceptual Modeling and Artificial Intelligence: Mutual Benefits from Complementary Worlds
Dominik Bork

TL;DR
This paper explores the intersection of conceptual modeling and artificial intelligence, highlighting their complementary strengths and potential mutual benefits for improved system understanding and processing.
Contribution
It introduces the CMAI workshop, emphasizing the exploration of how CM can enhance AI and vice versa, fostering interdisciplinary collaboration.
Findings
Conceptual models are comprehensible and reproducible knowledge representations.
AI techniques efficiently derive outputs but often lack explainability.
The workshop promotes interdisciplinary research between CM and AI.
Abstract
Conceptual modeling (CM) applies abstraction to reduce the complexity of a system under study (e.g., an excerpt of reality). As a result of the conceptual modeling process a human interpretable, formalized representation (i.e., a conceptual model) is derived which enables understanding and communication among humans, and processing by machines. Artificial Intelligence (AI) algorithms are also applied to complex realities (regularly represented by vast amounts of data) to identify patterns or to classify entities in the data. Aside from the commonalities of both approaches, a significant difference can be observed by looking at the results. While conceptual models are comprehensible, reproducible, and explicit knowledge representations, AI techniques are capable of efficiently deriving an output from a given input while acting as a black box. AI solutions often lack comprehensiveness and…
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Taxonomy
TopicsScientific Computing and Data Management · Data Visualization and Analytics · Advanced Database Systems and Queries
