Ontology-supported AI Model and Dataset Management

Abstract:Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems.

Artificial Intelligence (cs.AI)

arXiv:2608.21224 [cs.AI]

(or arXiv:2608.21224v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.21224 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), Beijing, China, 2024, pp. 1-6

https://doi.org/10.1109/INDIN58382.2024.10774524 Focus to learn more DOI(s) linking to related resources

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