Denotational Semantics for ODRL: Knowledge-Based Constraint Conflict Detection
Daham Mustafa, Diego Collarana, Yixin Peng, Rafiqul Haque, Christoph Lange-Bever, Christoph Quix, Stephan Decker

TL;DR
This paper introduces a denotational semantics framework for ODRL that enables conflict detection based on knowledge-base concepts, supporting cross-dataspace interoperability and verified with extensive benchmarks.
Contribution
It provides a formal semantics for ODRL constraints, ensuring sound conflict detection under incomplete knowledge and demonstrating practical validation with multiple knowledge bases.
Findings
Conflict detection is sound under incomplete knowledge.
Exclusive composition (xone) requires stronger axioms than and/or.
Framework supports cross-dataspace interoperability with graceful degradation.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Access Control and Trust · Adversarial Robustness in Machine Learning
