aiqbench analysis
This paper proposes the Violation Situation Pattern (VSP), an ontology framework that treats compliance violations as persistent entities within knowledge graphs rather than transient query results. By modeling violations with full lifecycle states and immutable audit trails, it enables legal knowledge systems to evolve rules without duplicating records or losing historical integrity. The approach is significant for building robust AI-driven governance tools where explainability and traceable decision histories are critical requirements.
The Violation Situation Pattern: Persistent Representation of Compliance Violations in Knowledge Graphs
arXiv:2606.03326v2 Announce Type: replace Abstract: Existing compliance pipelines identify violations as transient query results, leaving no persistent representation of the violation itself or its lifecycle, evidence, and audit history. We address this limitation with the Violation Situation Pattern (VSP), a reusable ontology pattern that extends the Situation pattern of Gangemi and Mika by modeling each detected violation as a persistent first-class graph entity. Each violation is associated with a rule identifier, temporal validity interval, lifecycle state, and evidence links to the affected entities, while immutable lifecycle events provide a complete and queryable audit trail. We instantiate VSP on the schema of a deployed legal knowledge graph integrating corporate governance and contract data, populated with public contract corpora containing violations with established ground truth, and operationalize four deontic compliance rules. Rather than evaluating detection algorithms, we evaluate the pattern's robustness under rule evolution. Results show that repeated rule execution creates no duplicate violations, modifications to rule definitions preserve complete audit histories, and expanding rule scope maintains violation identity and lifecycle continuity. A case study using 73 regulatory enforcement decisions further demonstrates that compliance rules can evolve to capture additional confirmed violations without compromising the integrity of stored identities, evidence, or audit histories. These findings show that VSP enables compliance knowledge graphs to evolve without sacrificing traceability, explainability, or historical consistency. The complete implementation, queries, SHACL shapes, and evaluation dataset are publicly available to support the reproducibility of the reported results.
Abstract:Existing compliance pipelines identify violations as transient query results, leaving no persistent representation of the violation itself or its lifecycle, evidence, and audit history. We address this limitation with the Violation Situation Pattern (VSP), a reusable ontology pattern that extends the Situation pattern of Gangemi and Mika by modeling each detected violation as a persistent first-class graph entity. Each violation is associated with a rule identifier, temporal validity interval, lifecycle state, and evidence links to the affected entities, while immutable lifecycle events provide a complete and queryable audit trail. We instantiate VSP on the schema of a deployed legal knowledge graph integrating corporate governance and contract data, populated with public contract corpora containing violations with established ground truth, and operationalize four deontic compliance rules. Rather than evaluating detection algorithms, we evaluate the pattern's robustness under rule evolution. Results show that repeated rule execution creates no duplicate violations, modifications to rule definitions preserve complete audit histories, and expanding rule scope maintains violation identity and lifecycle continuity. A case study using 73 regulatory enforcement decisions further demonstrates that compliance rules can evolve to capture additional confirmed violations without compromising the integrity of stored identities, evidence, or audit histories. These findings show that VSP enables compliance knowledge graphs to evolve without sacrificing traceability, explainability, or historical consistency. The complete implementation, queries, SHACL shapes, and evaluation dataset are publicly available to support the reproducibility of the reported results.
Artificial Intelligence (cs.AI)
arXiv:2606.03326 [cs.AI]
(or arXiv:2606.03326v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.03326 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 326 words
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