Real Time Reasoning in OWL2 for GDPR Compliance
It addresses GDPR compliance for organizations, offering a practical solution with real-time capabilities, though it is incremental as it builds on existing knowledge representation techniques.
This paper tackles the problem of real-time compliance checking for GDPR using knowledge representation, by encoding policies and regulations in a fragment of OWL2 called PL and developing a specialized reasoner PLR that achieves real-time performance through knowledge compilation and structural subsumption, with experimental analysis showing its effectiveness compared to other reasoners.
This paper shows how knowledge representation and reasoning techniques can be used to support organizations in complying with the GDPR, that is, the new European data protection regulation. This work is carried out in a European H2020 project called SPECIAL. Data usage policies, the consent of data subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2 called PL (policy language); compliance checking and policy validation are reduced to subsumption checking and concept consistency checking. This work proposes a satisfactory tradeoff between the expressiveness requirements on PL posed by the GDPR, and the scalability requirements that arise from the use cases provided by SPECIAL's industrial partners. Real-time compliance checking is achieved by means of a specialized reasoner, called PLR, that leverages knowledge compilation and structural subsumption techniques. The performance of a prototype implementation of PLR is analyzed through systematic experiments, and compared with the performance of other important reasoners. Moreover, we show how PL and PLR can be extended to support richer ontologies, by means of import-by-query techniques. PL and its integration with OWL2's profiles constitute new tractable fragments of OWL2. We prove also some negative results, concerning the intractability of unrestricted reasoning in PL, and the limitations posed on ontology import.