• GDPR-compliant Data Sharing Employing Consent, Contracts, and Licenses. • Validating the CCV checks by utilizing SHACL which is more semantically compliant. • Introducing SHACL repairs to automatically fix data inconsistencies. • Securing the contract signing process by utilizing digital signatures. • Digital assets licensing through DALICC for the improvement of the CCV tool. GDPR defines six legal bases, at least one of which needs to be followed in order to process (or share) personally identifiable data in a lawful manner. Most of the research today is centered around the legal bases of consent and contracts. This limits the options for legal bases that one can select (or use) for data sharing, especially in circumstances where there is a need to use mul-tiple legal bases. For example, one can consent to share data but may want to place restrictions on how it can be used, which requires a license (an extension/add-on to data sharing contracts) in scenarios, where digital assets licensing is involved. Overcoming these limitations and en-abling data sharing via multiple legal bases require combining multiple legal bases. However, incorporating additional (or multiple) legal bases, such as licenses (as an add-on to contracts), in a GDPR-compliant manner remains a challenging task. This is because combining multiple legal bases requires an understanding of each individual legal basis—a task challenging in it-self—and designing a system in a manner that is both compliant with regulatory requirements and practically pertinent. Therefore, in this paper, we present our semantic-based approach and tool that enables GDPR-compliant data sharing via multiple legal bases, consent, and contracts (using licenses as an add-on). This work extends our previous work, GDPR Contract Com-pliance Verification (CCV) tool, which enables GDPR-compliant data sharing via consent and contracts only. We add licenses as a further add-on to contracts, make our previous work more semantically compliant by utilizing SHACL validation for compliance checking, secure the con-tract signing process with digital signatures, introduce SHACL repairs to automatically fix data inconsistencies, and evaluate the performance of the tool and the SHACL components. We demonstrate the effectiveness of SHACL and the enhancement of the tool with GDPR-complaint data sharing based on multiple legal bases by performance testing.

Soil health is fundamental to environmental sustainability and food security, yet relevant knowledge remains fragmented across diverse sources, hindering its effective application. Knowledge graphs (KGs) offer a robust solution by integrating disparate information into a structured, semantically rich format. Addressing this need, this paper presents an ontology-compliant soil health KG derived from domain literature, and the semi-automated, human-in-the-loop pipeline developed to construct it. Our pipeline leverages large language models to accelerate knowledge extraction, while incorporating expert oversight to ensure ontological compliance and accuracy. The resulting KG integrates unstructured knowledge into 11,715 RDF triples representing 2,018 entities, including 1,786 soil-related concepts. The KG’s fidelity was confirmed by soil scientists through a validation process involving competency questions. The KG, supporting ontology, and the source code of the pipeline are made publicly available.

Climate change is a heated discussion topic in public arenas such as social media. Both texts and visuals play key roles in the debate, as they can complement, contradict, or reinforce each other in nuanced ways. It is therefore urgently needed to study the messages as multimodal objects to better understand the polarized debate about climate change impacts and policies. Multimodal representation models such as CLIP are known to be able to transfer knowledge across domains and modalities, enabling the investigation of textual and visual semantics together. Yet they are not directly able to distinguish the nuances between supporting and sceptic climate change stances. This paper explores a simple but effective strategy combining modality fusion and domain-knowledge enhancing to prepare CLIP-based models with knowledge of climate change stances. A multimodal Dutch Twitter dataset is collected and experimented with the proposed strategy, which increased the macro-average F1 score across stances from 51% to 86%. The outcomes can be applied in both data science and public policy studies, to better analyse how the combined use of texts and visuals generates meanings during debates, in the context of climate change and beyond.

Consent is one of GDPR’s lawful bases for data processing and specific requirements for it apply. Consent should be specific, unambiguous and most of all informed. However, an informed consent request does not guarantee having individuals who are aware of what it means to consent and the implications that follow. Consent is often given blindly now, in particular because of information overload from long privacy policies written in legal language and complex interface designs that cause consent fatigue on the users' side. This paper presents a knowledge graph-based user interface for consent solicitation, which uses gamification to raise the legal awareness and ease individual’s comprehension of consent. The knowledge graph models informed consent in a machine-readable format and provides a unified consent model to all entities involved in the data sharing process. The evaluation shows that with the help of gamification, the interface can raise individuals' average legal awareness to 92.86%.

The energy sector is becoming an important showcase for data economy, because of the societal, environmental and business value that can be created based on the vast amounts of newly available energy-related data. Utilizing the explosive growth in Big Data technology solutions, and the steady march of open data initiatives, this development is mainly driven by the requirements regarding CO2emissions reduction, and the vision of environmentally sustainable energy consumption and production in the Smart Grid. The market for energy efficient home appliances, renewable energy generation equipment, and accompanying services that monitor, control and increase efficiency of energy consumption and own energy generation is drastically increasing, pulling the demand and expectations of the mass consumers. In addition to almost well-established automated metering and demand response business models, one interesting new opportunity is emerging: based on the data directly available from home devices and appliances, their manufacturers can develop new customer engagement approaches and added-value services. In this paper we describe the preliminary architecture that integrates Big Data and semantic overlay components, developed in the project OpenFridge with the goal to create data economy for energy efficiency information. The project addresses different challenges of offering specific type of energy efficiency information to various interested stakeholders (appliance manufacturers, end users, utilities, municipalities, etc.) under the new access mechanisms and business models. The initial service focuses on energy consumption analysis of large household appliance(s) such as a fridge, and involves the real end user communities.

This paper highlights how the domain of Smart Cities is often modeled by ontologies to create applications and services that are highly flexible, (re)configurable, and inter-operable. However, ontology repositories and their accompanying reasoning and rule languages face the disadvantage of bad runtime behavior, especially if the models grow large in size. We propose an architecture that uses tools and methods from the domain of Big Data processing in conjunction with an ontology repository and a rule engine to overcome potential performance bottlenecks that will occur in this scenario.

This paper presents a semantic policy adaptation technique and its applications in the context of smart building setups. We study how semantic rules which are created for one set of contextual conditions are affected when one of the context parameters changes. In particular, rules for triggering alerts and monitoring appliances and their adaptation with changing contexts have been studied in detail. We then describe how this changing context triggers changes in other related context parameters. This technique has been implemented to demonstrate its feasibility, evaluated and positively accepted during trials with users of a real-life semantically empowered smart building setup.

With the rapid development of the Web in the direction Social Media, the number of communication possibilities has increased exponentially, bringing new challenges and opportunities for companies to build and shape their reputation online as well as to engage and maintain good relations with their customers. In this paper we describe how semantic technologies enable scalable, effective and efficient on-line communication. We propose four different ways in which semantics can be used for this purpose. First, we discuss semantic analysis of communication items based on 'classical' semantics, such as natural language processing. Second, we look at semantics as a channel, viewing Linked Open Data vocabularies not only as terminological assets but as communication channels. Third, we demonstrate how semantics provide the methodologies and tools for content modeling by means of ontologies. Finally, we outline how semantics through semantic matchmaking enable semi-automatic assignment and distribution of content to channels and vice-versa.

Mobile computing enables end-users not only to access and consume information on-the-go but also to act as service and content providers. With the right tools, end-users can create small services on their mobiles and share valuable and context-aware information with others.

We introduce community-driven ontology matchingalignment and demonstrate the added value to conventional ontology matchingalignment solutions solutions of being community-driven. Further, we present an approach to construction of a prototype for community-driven ontology matchingalignment. The prototype was made available on the Web, and its usage was observed.

We analyze typical Web portals supporting communication, data sharing and activities of former classmates. The inflexibility and restrictions imposed on users of such portals are demonstrated to support the thesis that introduction of community-driven ontology management is crucial for full-fledged satisfaction of the user needs on the semantic Web.

We introduce the concept of community-driven ontology management and demonstrate the added value to conventional ontology management of being community-driven. Further, we present an implementation of an infrastructure supporting community-driven ontology management. The implemented infrastructure was deployed as a part of the intranet at DERI - Digital Enterprise Research Institute, and the community's response and behavior were observed. The results obtained prove feasibility and advantages of community-driven ontology management.