20 research outputs found

    ODRL Policy Modelling and Compliance Checking

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    This paper addresses the problem of constructing a policy pipeline that enables compliance checking of business processes against regulatory obligations. Towards this end, we propose an Open Digital Rights Language (ODRL) profile that can be used to capture the semantics of both business policies in the form of sets of required permissions and regulatory requirements in the form of deontic concepts, and present their translation into Answer Set Programming (via the Institutional Action Language (InstAL)) for compliance checking purposes. The result of the compliance checking is either a positive compliance result or an explanation pertaining to the aspects of the policy that are causing the noncompliance. The pipeline is illustrated using two (key) fragments of the General Data Protect Regulation, namely Articles 6 (Lawfulness of processing) and Articles 46 (Transfers subject to appropriate safeguards) and industrially-relevant use cases that involve the specification of sets of permissions that are needed to execute business processes. The core contributions of this paper are the ODRL profile, which is capable of modelling regulatory obligations and business policies, the exercise of modelling elements of GDPR in this semantic formalism, and the operationalisation of the model to demonstrate its capability to support personal data processing compliance checking, and a basis for explaining why the request is deemed compliant or not

    An Application of Declarative Languages in Distributed Architectures: ASP and DALI Microservices

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    In this paper we introduce an approach to the possible adoption of Answer Set Programming (ASP) for the definition of microservices, which are a successful abstraction for designing distributed applications as suites of independently deployable interacting components. Such ASP-based components might be employed in distributed architectures related to Cloud Computing or to the Internet of Things (IoT), where the ASP microservices might be usefully coordinated with intelligent logic-based agents. We develop a case study where we consider ASP microservices in synergy with agents defined in DALI, a well-known logic-based agent-oriented programming language developed by our research group

    RDF graph validation using rule-based reasoning

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    The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application's supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator's performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation "Validatrr", based on N3Logic and the EYE reasoner. Our approach - supporting an equivalent number of constraint types compared to the state of the art - better explains the root cause of the violations due to the reasoner's generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions

    OWL Reasoners still useable in 2023

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    In a systematic literature and software review over 100 OWL reasoners/systems were analyzed to see if they would still be usable in 2023. This has never been done in this capacity. OWL reasoners still play an important role in knowledge organisation and management, but the last comprehensive surveys/studies are more than 8 years old. The result of this work is a comprehensive list of 95 standalone OWL reasoners and systems using an OWL reasoner. For each item, information on project pages, source code repositories and related documentation was gathered. The raw research data is provided in a Github repository for anyone to use

    Deciding FO-Rewritability of Ontology-Mediated Queries in Linear Temporal Logic

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    Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

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    Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

    Deciding FO-rewritability of ontology-mediated queries in linear temporal logic

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    Our concern is the problem of determining the data complexity of answering an ontology-mediated query (OMQ) given in linear temporal logic LTL over (Z,<) and deciding whether it is rewritable to an FO(<)-query, possibly with extra predicates. First, we observe that, in line with the circuit complexity and FO-definability of regular languages, OMQ answering in AC0, ACC0 and NC1 coincides with FO(<,\equiv)-rewritability using unary predicates x \equiv 0 mod n), FO(<,MOD)-rewritability, and FO(RPR)-rewritability using relational primitive recursion, respectively. We then show that deciding FO(<)-, \FO(<,\equiv)- and FO(<,MOD)-rewritability of LTL OMQs is ExpSpace-complete, and that these problems become PSpace-complete for OMQs with a linear Horn ontology and an atomic query, and also a positive query in the cases of FO(<)- and FO(<,\equiv)-rewritability. Further, we consider FO(<)-rewritability of OMQs with a binary-clause ontology and identify OMQ classes, for which deciding it is PSpace-, Pi_2^p- and coNP-complete
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