A hierarchy of faithfulness criteria for knowledge base completion

Abstract:Knowledge graph completion is evaluated by ranking observed triples above randomly corrupted ones, which treats every unobserved fact as false. When the object being completed is a description logic knowledge base rather than a plain graph, the open world assumption and deductive closure make this inadequate: relative to the knowledge base, a candidate axiom is entailed, contradictory, or undetermined, and a model that cannot separate a logically impossible axiom from a plausible novel one is not merely less accurate but semantically incorrect. We ask what it means for a knowledge base completion model to be logically faithful, and whether current embedding models are. We define a hierarchy of four increasingly strict criteria, discrimination, logical admissibility, monotonic logical faithfulness, and probabilistic logical faithfulness, and prove that they form a strict chain of implications. We ground the strongest criterion in the relative model count $P(\alpha\mid\mathcal{O}) = \#(\mathcal{O}\cup\{\alpha\})/\#(\mathcal{O})$, which recovers the trichotomy at its endpoints and ranks undetermined axioms in between. Evaluating knowledge graph and logic-geometric embedding models on $\mathcal{EL}$ ontologies, with entailed, contradictory, and undetermined test sets generated by a reasoner, we find that ranking accuracy does not imply logical faithfulness and that none of the evaluated models is faithful across the hierarchy. The code is available at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2609.27863 [cs.AI]

(or arXiv:2609.27863v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.27863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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