CONTO (CONfiguration ONTOlogy and TOols) addresses the challenges of vendor lock-in and costly evaluations in product configuration systems through an ontology-based semantic framework that establishes interoperability without reinventing existing solver technologies. Our approach provides dual representations-instance-based Configuration Vocabulary and OWL DL-based formalism-with tooling to transform product models into programs for various platforms. By decoupling modelling from configuration processing, CONTO creates an abstraction layer that preserves existing investments while enabling integration with emerging AI tools. Our implementation shows practical applications supporting both commercial and open-source configurators.

This paper describes our approach for leveraging LLMs to generate definitions and descriptions for ontology terms. Our approach is grounded in the need for detailed and accurate representation of (domain-specific) Knowledge Graphs, and it aims at speeding up the process of generating such text. We outline our approach, including the problems that we encountered, and the solution we propose to overcome them. Our approach is currently in use in an industrial setting.

This paper addresses the critical challenge of fragmented data and knowledge in high-pressure die-casting environments, where the lack of integrated information hampers effective troubleshooting and compliance with emerging transparency requirements. We developed a comprehensive semantic model that integrates distributed data sources and expert knowledge into a unified knowledge graph framework, explicitly connecting manufacturing processes, failures, metrics, and countermeasures through formalized semantic relationships. Our implementation shows how the resulting architecture successfully transforms traditionally siloed industrial data into an interconnected knowledge representation that distinguishes between specified expert knowledge and actual operational data, enabling systematic reasoning about cause-effect relationships throughout the manufacturing process. The approach provides significant value by enhancing manufacturing transparency and decision support while aligning with Industry 5.0 principles and emerging regulatory frameworks for explainable industrial systems, ultimately supporting more sustainable and efficient manufacturing processes.

Effective knowledge representation plays a pivotal role in harnessing the full potential of domain-specific information. Through tools like Infinity Maps, domain knowledge can be easily captured in a visual manner. However, translating these visually intuitive representations to formal, machine-processable formats often necessitates expert knowledge, thereby creating a significant barrier between domain experts and knowledge engineers. While domain experts possess deep understanding of their respective domains, they often lack the formalisation skills required to transform this knowledge into machine-readable formats. Conversely, knowledge engineers can design and implement sophisticated knowledge graphs, but may not have access to the domain-specific expertise necessary for effective knowledge representation. To address this challenge, we propose a novel approach that leverages SHACL (Shape Constraint Language) rules to transform visual domain knowledge expressed as Infinity Maps into knowledge graphs. Our method enables domain experts to define their knowledge structures using familiar Infinity Map representations, which are then transformed into standardised knowledge graphs compliant with the SHACL standard.

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.

The publication and interchange of RDF datasets online has experienced significant growth in recent years, promoted by different but complementary efforts, such as Linked Open Data, the Web of Things and RDF stream processing systems. However, the current Linked Data infrastructure does not cater for the storage and exchange of sensitive or private data. On the one hand, data publishers need means to limit access to confidential data (e.g. health, financial, personal, or other sensitive data). On the other hand, the infrastructure needs to compress RDF graphs in a manner that minimises the amount of data that is both stored and transferred over the wire. In this paper, we demonstrate how HDT - a compressed serialization format for RDF - can be extended to cater for supporting encryption. We propose a number of different graph partitioning strategies and discuss the benefits and tradeoffs of each approach.

In this paper, we introduce the Data Licenses Clearance Center system, which not only provides a library of machine readable licenses but also allows users to compose their own license. A demonstrator can be found at https://www.dalicc.net.

: This paper describes the Data Licenses Clearance Center, a software framework that supports the cost-efficient and transparent resolution of licensing conflicts that occur in the reutilization of digital assets. DALICC provides a library of machine readable standard licenses and allows users to compose arbitrary custom licenses. In addition, the system supports the clearance of rights issues by providing users with information about the equivalence, similarity and compatibility of licenses. A public beta version of the system is available at https://www.dalicc.net/.

Managing privacy and understanding handling of personal data has turned into a fundamental right, at least within the European Union, with the General Data Protection Regulation (GDPR) being enforced since May 25th 2018. This has led to tools and services that promise compliance to GDPR in terms of consent management and keeping track of personal data being processed. The information recorded within such tools, as well as that for compliance itself, needs to be interoperable to provide sufficient transparency in its usage. Additionally, interoperability is also necessary towards addressing the right to data portability under GDPR as well as creation of user-configurable and manageable privacy policies. We argue that such interoperability can be enabled through agreement over vocabularies using linked data principles. The W3C Data Privacy Vocabulary and Controls Community Group (DPVCG) was set up to jointly develop such vocabularies towards interoperability in the context of data privacy. This paper presents the resulting Data Privacy Vocabulary (DPV), along with a discussion on its potential uses, and an invitation for feedback and participation.

In this paper we introduce the Data Licenses Clearance Center, which provides a library of machine readable standard licenses and allows users to compose arbitrary licenses. In addition, the system supports the clearance of rights issues by providing users with information about the equivalence, similarity and compatibility of licenses. A beta version of the system is available at https://www.dalicc.net/.

In this paper, we propose an extension of the Open Digital Right Language for modeling well-known licenses and propose an approach to automatically check license compatibility.

Currently there is no single dominating technology for building product configurator systems. While research often focuses on a single technology/paradigm, building an industrial-scale product configurator system will almost always require the combination of various technologies for different aspects of the system (knowledge representation, reasoning, solving, user interface, etc.) This paper demonstrates how to build such a hybrid system and how to integrate various technologies and leverage their respective strengths. Due to the increasing popularity of the industrial knowledge graph we utilize Semantic Web technologies (RDF, OWL and the Shapes Constraint Language (SHACL)) for knowledge representation and integrate it with Answer Set Programming (ASP), a well-established solving paradigm for product configuration.

The amount of raw data exchanged via web protocols is steadily increasing. Although the Linked Data infrastructure could potentially be used to selectively share RDF data with different individuals or organisations, the primary focus remains on the unrestricted sharing of public data. In order to extend the Linked Data paradigm to cater for closed data, there is a need to augment the existing infrastructure with robust security mechanisms. At the most basic level both access control and encryption mechanisms are required. In this paper, we propose a flexible and dynamic mechanism for securely storing and efficiently querying RDF datasets. By employing an encryption strategy based on Functional Encryption (FE) in which controlled data access does not require a trusted mediator, but is instead enforced by the cryptographic approach itself, we allow for fine-grained access control over encrypted RDF data while at the same time reducing the administrative overhead associated with access control management.

Legislative compliance assessment tools are commonly used by companies to help them to understand their legal obligations. One of the primary limitations of existing tools is that they tend to consider each regulation in isolation. In this paper, we propose a flexible and modular compliance assessment framework that can support multiple legislations. Additionally, we describe our extension of the Open Digital Rights Language (ODRL) so that it can be used not only to represent digital rights but also legislative obligations, and discuss how the proposed model is used to develop a flexible compliance system, where changes to the obligations are automatically reflected in the compliance assessment tool. Finally, we demonstrate the effectiveness of the proposed approach through the development of a General Data Protection Regulatory model and compliance assessment tool.