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Denotational Semantics for ODRL: Knowledge-Based Constraint Conflict Detection

arXiv:2602.19883v1h-index: 13
Originality Incremental advance
AI Analysis

This work addresses cross-dataspace interoperability for policy conflict detection in ODRL, providing a formal framework with sound guarantees, but it is incremental as it builds on existing ODRL semantics.

The paper tackled the problem of detecting conflicts in ODRL policies due to unspecified external domain knowledge, by presenting a denotational semantics that maps constraints to knowledge-base concepts and reduces conflict detection to denotation intersection with sound three-valued verdicts. The result was validated with 154 benchmarks across diverse knowledge bases, showing agreement between theorem provers and revealing that exclusive composition requires stronger axioms.

ODRL's six set-based operators -- isA, isPartOf, hasPart, isAnyOf, isAllOf, isNoneOf -- depend on external domain knowledge that the W3C specification leaves unspecified. Without it, every cross-dataspace policy comparison defaults to Unknown. We present a denotational semantics that maps each ODRL constraint to the set of knowledge-base concepts satisfying it. Conflict detection reduces to denotation intersection under a three-valued verdict -- Conflict, Compatible, or Unknown -- that is sound under incomplete knowledge. The framework covers all three ODRL composition modes (and, or, xone) and all three semantic domains arising in practice: taxonomic (class subsumption), mereological (part-whole containment), and nominal (identity). For cross-dataspace interoperability, we define order-preserving alignments between knowledge bases and prove two guarantees: conflicts are preserved across different KB standards, and unmapped concepts degrade gracefully to Unknown -- never to false conflicts. A runtime soundness theorem ensures that design-time verdicts hold for all execution contexts. The encoding stays within the decidable EPR fragment of first-order logic. We validate it with 154 benchmarks across six knowledge base families (GeoNames, ISO 3166, W3C DPV, a GDPR-derived taxonomy, BCP 47, and ISO 639-3) and four structural KBs targeting adversarial edge cases. Both the Vampire theorem prover and the Z3 SMT solver agree on all 154 verdicts. A key finding is that exclusive composition (xone) requires strictly stronger KB axioms than conjunction or disjunction: open-world semantics blocks exclusivity even when positive evidence appears to satisfy exactly one branch.

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