Ontology-supported AI Model and Dataset Management
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 gap
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 26%semantica-agi/semantica →
“Possibly related via embedding similarity 0.56 (not asserted). Timestamp check: artifact slightly before paper (-50d).”
- PossiblePossibly related (embedding) · 65%The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix →
- PossiblePossibly related (embedding) · 61%Enterprise AI agents are only as reliable as the messiest documents behind them →
- LinkedLinked via arxiv author · 85%Jan Novacek →
“Ontology-supported AI Model and Dataset Management”
- LinkedLinked via arxiv author · 85%Ali Ahari →
“Ontology-supported AI Model and Dataset Management”
- LinkedLinked via arxiv author · 85%Tobias Müller →
“Ontology-supported AI Model and Dataset Management”
- LinkedLinked via arxiv author · 85%Sebastian Reiter →
“Ontology-supported AI Model and Dataset Management”
- LinkedLinked via arxiv author · 85%Alexander Viehl →
“Ontology-supported AI Model and Dataset Management”