arXiv subjects

Sabrina Kirrane

Publications and source records attributed to Sabrina Kirrane.

16 recordsLinked to original sources

arXiv2026

Sort-Stratified Semantics for Temporal Conflict Detection in ODRL Policies

In the Open Digital Rights Language (ODRL), temporal constraints range over two sorts, instants and durations, but the comparison operators do not distinguish them. The same operator thus means "earlier instant" or "shorter duration," leaving conflict detection between two policies unsound. We resolve this by sort stratification: each temporal operand is typed to one of two ordered domains, points in time or amounts of time. Each constraint then denotes an interval, and conflict reduces to interval comparison under a three-valued verdict (Conflict, Compatible, Unknown). We characterise the check's decidability across a static and a runtime fragment, prove it sound, and evaluate it on a benchmark of policy problems compiled to TPTP and SMT-LIB, available as an artefact.

cs.LO↗

arXiv2026

Axis-Aligned Semantics for ODRL: Resolving Dimensional Ambiguity in Policy Constraints

The Open Digital Rights Language (ODRL) represents policy constraints as triples of a left operand, an operator, and a value. Several spatial operands, however, range over multi-axis domains such as width, height, and depth, while the constraint syntax provides no explicit axis identity. As a result, policy engines cannot determine whether multiple constraints apply to the same axis or different ones, making conflict detection unsound or incomplete. We resolve this ambiguity by axis decomposition, replacing multi-axis operands with axis-specific scalar operands over totally ordered domains. Each constraint then denotes an interval per axis and each policy an axis-aligned box, reducing conflict detection to box comparison. We define a three-valued semantics (Conflict, Compatible, Unknown), prove the decomposition sound and backward compatible with ODRL, instantiate it as ODRL Axis-Aligned Profile (OAAP), and validate it on a benchmark of 256 ODRL policy problems, each expressed in Turtle and compiled to first-order (TPTP) and SMT-LIB form, using Vampire, E, Z3, and cvc5.

cs.CL↗

arXiv2026

A Semantic Encoding of Object Centric Event Data

The Object-Centric Event Data (OCED) is a novel meta-model aimed at providing a common ground for process data records centered around events and objects. One of its objectives is to foster interoperability and process information exchange. In this context, the integration of data from different providers, the combination of multiple processes, and the enhancement of knowledge inference are novel challenges. Semantic Web technologies can enable the creation of a machine-readable OCED description enriched through ontology-based relationships and entity categorization. In this paper, we introduce an approach built upon Semantic Web technologies for the realization of semantic-enhanced OCED, with the aim to strengthen process data reasoning, interconnect information sources, and boost expressiveness.

cs.IR↗

arXiv2025

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star

In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage traces and accounting for temporal aspects such as start times and deadlines, as obligations may evolve over times into different states, such as fulfilled, violated, or expired. While several solutions have been proposed for obligation monitoring, they often lack formal semantics or provide limited support for reasoning over obligation states. To address these limitations, we extend GUCON, a policy framework grounded in the formal semantics of SPAQRL graph patterns, to explicitly model the temporal aspects of an obligation. This extension enables the expressing of temporal obligations and supports continuous monitoring of their evolving states based on usage traces stored in temporal knowledge graphs. We demonstrate how this extended model can be represented using RDF-star and SPARQL-star and propose an Obligation State Manager that monitors obligation states and assess their compliance with respect to usage traces. Finally, we evaluate both the extended model and its prototype implementation.

cs.CR↗

arXiv2023

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

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.

cs.AI↗

arXiv2023

A Blockchain-driven Architecture for Usage Control in Solid

Decentralization initiatives like Solid enable data owners to control who has access to their data and to stimulate innovation by creating both application and data markets. Once data owners share their data with others, though, it is no longer possible for them to control how their data are used. To address this issue, we propose a usage control architecture to monitor compliance with usage control policies. To this end, our solution relies on blockchain and trusted execution environments. We demonstrate the potential of the architecture by describing the various workflows needed to realize a motivating use case scenario for data markets. Additionally, we discuss the merits of the approach from privacy, security, integrateability, and affordability perspectives.

cs.MA↗

arXiv2023

Blockchain based Resource Governance for Decentralized Web Environments

Decentralization initiatives such as Solid and ActivityPub aim to give data owners more control over their data and to level the playing field by enabling small companies and individuals to gain access to data, thus stimulating innovation. However, these initiatives typically employ access control mechanisms that cannot verify compliance with usage conditions after access has been granted to others. In this paper, we extend the state of the art by proposing a resource governance conceptual framework, entitled ReGov, that facilitates usage control in decentralized web environments. We subsequently demonstrate how our framework can be instantiated by combining blockchain and trusted execution environments. Through blockchain technologies, we record policies expressing the usage conditions associated with resources and monitor their compliance. Our instantiation employs trusted execution environments to enforce said policies, inside data consumers' devices.} We evaluate the framework instantiation through a detailed analysis of requirements derived from a data market motivating scenario, as well as an assessment of the security, privacy, and affordability aspects of our proposal.

cs.NI↗

arXiv2022

Usage Control Specification, Enforcement, and Robustness: A Survey

The management of data and digital assets poses various challenges, including the need to adhere to legal requirements with respect to personal data protection and copyright. Usage control technologies could be used by software platform providers to manage data and digital assets responsibly and to provide more control to data and digital asset owners. In order to better understand the potential of various usage control proposals, we collate and categorize usage control requirements, compare the predominant usage control frameworks based on said requirements, and identify existing challenges and opportunities that could be used to guide future research directions.

cs.CR↗

arXiv2022

Governance of Autonomous Agents on the Web: Challenges and Opportunities

The study of autonomous agents has a long tradition in the Multiagent Systems and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.

cs.MA↗

arXiv2021

Intelligent Software Web Agents: A Gap Analysis

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning, and data integration. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological opportunities and challenges, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community. We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements from the semantic web agent literature. We subsequently propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion in relation to existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things.

cs.AI↗

arXiv2021

Knowledge Graphs

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

cs.AI↗

arXiv2021

The SPECIAL-K Personal Data Processing Transparency and Compliance Platform

The European General Data Protection Regulation (GDPR) brings new challenges for companies who must ensure they have an appropriate legal basis for processing personal data and must provide transparency with respect to personal data processing and sharing within and between organisations. Additionally, when it comes to consent as a legal basis, companies need to ensure that they comply with usage constraints specified by data subjects. This paper presents the policy language and supporting ontologies and vocabularies, developed within the SPECIAL EU H2020 project, which can be used to represent data usage policies and data processing and sharing events. We introduce a concrete transparency and compliance architecture, referred to as SPECIAL-K, that can be used to automatically verify that data processing and sharing complies with the data subjects consent. Our evaluation, based on a new compliance benchmark, shows the efficiency and scalability of the system with increasing number of events and users.

cs.CR↗

arXiv2020

Query Based Access Control for Linked Data

In recent years we have seen significant advances in the technology used to both publish and consume Linked Data. However, in order to support the next generation of ebusiness applications on top of interlinked machine readable data suitable forms of access control need to be put in place. Although a number of access control models and frameworks have been put forward, very little research has been conducted into the security implications associated with granting access to partial data or the correctness of the proposed access control mechanisms. Therefore the contributions of this paper are two fold: we propose a query rewriting algorithm which can be used to partially restrict access to SPARQL 1.1 queries and updates; and we demonstrate how a set of criteria, which was originally used to verify that an access control policy holds over different database states, can be adapted to verify the correctness of access control via query rewriting.

cs.DB↗

arXiv2020

Why model why? Assessing the strengths and limitations of LIME

When it comes to complex machine learning models, commonly referred to as black boxes, understanding the underlying decision making process is crucial for domains such as healthcare and financial services, and also when it is used in connection with safety critical systems such as autonomous vehicles. As such interest in explainable artificial intelligence (xAI) tools and techniques has increased in recent years. However, the effectiveness of existing xAI frameworks, especially concerning algorithms that work with data as opposed to images, is still an open research question. In order to address this gap, in this paper we examine the effectiveness of the Local Interpretable Model-Agnostic Explanations (LIME) xAI framework, one of the most popular model agnostic frameworks found in the literature, with a specific focus on its performance in terms of making tabular models more interpretable. In particular, we apply several state of the art machine learning algorithms on a tabular dataset, and demonstrate how LIME can be used to supplement conventional performance assessment methods. In addition, we evaluate the understandability of the output produced by LIME both via a usability study, involving participants who are not familiar with LIME, and its overall usability via an assessment framework, which is derived from the International Organisation for Standardisation 9241-11:1998 standard.

cs.LG↗

arXiv2020

Towards Querying in Decentralized Environments with Privacy-Preserving Aggregation

The Web is a ubiquitous economic, educational, and collaborative space. However, it also serves as a haven for personal information harvesting. Existing decentralised Web-based ecosystems, such as Solid, aim to combat personal data exploitation on the Web by enabling individuals to manage their data in the personal data store of their choice. Since personal data in these decentralised ecosystems are distributed across many sources, there is a need for techniques to support efficient privacy-preserving query execution over personal data stores. Towards this end, in this position paper we present a framework for efficient privacy preserving federated querying, and highlight open research challenges and opportunities. The overarching goal being to provide a means to position future research into privacy-preserving querying within decentralised environments.

cs.DB↗

arXiv2020

Machine Understandable Policies and GDPR Compliance Checking

The European General Data Protection Regulation (GDPR) calls for technical and organizational measures to support its implementation. Towards this end, the SPECIAL H2020 project aims to provide a set of tools that can be used by data controllers and processors to automatically check if personal data processing and sharing complies with the obligations set forth in the GDPR. The primary contributions of the project include: (i) a policy language that can be used to express consent, business policies, and regulatory obligations; and (ii) two different approaches to automated compliance checking that can be used to demonstrate that data processing performed by data controllers / processors complies with consent provided by data subjects, and business processes comply with regulatory obligations set forth in the GDPR.

cs.CY↗