• 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.

Abstract We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. There are many real-world scenarios in which the amount of data is severely limited (e.g. health industry, rare anomalies). Prior knowledge can be used to tackle this task. In KGTN, one can use a single knowledge source at once. The purpose of this study is to investigate the possibility of combining multiple knowledge sources. We evaluate it with different embeddings in a few-shot image classification task. Our model is partially trained on $$k \in \{ 1, 2, 5, 10\}$$ k ∈ { 1 , 2 , 5 , 10 } samples. We also construct a new knowledge source – Wikidata embeddings – and evaluate it with KGTN and KGTN-ens. With ResNet50, our approach outperforms KGTN in terms of the top-5 accuracy on the ImageNet-FS dataset for the majority of tested settings. For $$k \in \{ 1, 2, 5, 10\}$$ k ∈ { 1 , 2 , 5 , 10 } respectively, we obtained +0.63/+0.58/+0.43/+0.26 pp. (novel classes) and +0.26/+0.25/+0.32/–0.04 pp. (all classes).

Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain personally identifiable information. To address the above challenges, this paper presents a novel semantic technology-based framework that enables data sharing in a GDPR-compliant manner while ensuring that the data shared is interoperable, contains required context information, is of acceptable quality, and is accurate and trustworthy. The proposed framework also accounts for the edge/fog, an upcoming computing paradigm for the IoT to support real-time decisions. We evaluate the performance of the proposed framework with two different edge and fog-edge scenarios using resource-constrained IoT devices, such as the Raspberry Pi. In addition, we also evaluate shared data quality, interoperability and veracity. Our key finding is that the proposed framework can be employed on IoT devices with limited resources due to its low CPU and memory utilization for analytics operations and data transformation and migration operations. The low overhead of the framework supports real-time decision making. In addition, the 100% accuracy of our evaluation of the data quality and veracity based on 180 different observations demonstrates that the proposed framework can guarantee both data quality and veracity.

Food security is currently a major concern due to the growing global population, the exponential increase in food demand, the deterioration of soil quality, the occurrence of numerous diseases, and the effects of climate change on crop yield. Sustainable agriculture is necessary to solve this food security challenge. Disruptive technologies, such as of artificial intelligence, especially, deep learning techniques can contribute to agricultural sustainability. For example, applying deep learning techniques for early disease classification allows us to take timely action, thereby helping to increase the yield without inflicting unnecessary environmental damage, such as excessive use of fertilisers or pesticides. Several studies have been conducted on agricultural sustainability using deep learning techniques and also semantic web technologies such as ontologies and knowledge graphs. However, the three major challenges remain: (i) the lack of explainability of deep learning-based systems (e.g. disease information), especially to non-experts like farmers; (ii) a lack of contextual information (e.g. soil or plant information) and domain-expert knowledge in deep learning-based systems; and (iii) the lack of pattern learning ability of systems based on the semantic web, despite their ability to incorporate domain knowledge. Therefore, this paper presents the work on disease classification, addressing the challenges as mentioned earlier by combining deep learning and semantic web technologies, namely ontologies and knowledge graphs. The findings are: (i) 0.905 (90.5%) prediction accuracy on large noisy dataset; (ii) ability to generate user-level explanations about disease and incorporate contextual and domain knowledge; (iii) the average prediction latency of 3.8514 s on 5268 samples; (iv) 95% of users finding the explanation of the proposed method useful; and (v) 85% of users being able to understand generated explanations easily—show that the proposed method is superior to the state-of-the-art in terms of performance and explainability and is also suitable for real-world scenarios.

Smell and taste disorders have become a more prominent issue due to their association with Covid-19, and their impact on quality of life and health outcomes. However, pertinent information regarding these disorders is often inaccessible and poorly organized, with the majority of data stored solely in clinical data repositories. To rectify this, a technological solution capable of digitizing, semantically modeling, and integrating health data is necessary. The knowledge graph, an emerging technology capable of organizing inconsistent and heterogeneous health data and inferring implicit knowledge, presents a viable solution to this problem. In pursuit of the aforementioned goal, an existing ontology pertaining to smell and taste disorders was enriched by introducing additional relevant concepts and relationships. Subsequently, a knowledge graph was constructed based on the defined ontology and patients’ data. The resultant knowledge graph was subjected to a rigorous evaluation, encompassing dimensions such as completeness, coherency, coverage, and succinctness. The evaluation established the effectiveness and usability of the knowledge graph, with only minor issues detected through the OOPS! pitfall scanner. Furthermore, as a proof-of-concept for clinical application, a user interface was created, enabling users to access pertinent information concerning smell and taste disorders, including causative factors, medications, and etiology, among others. The interface generates a graph-based structure based on the selected question from a drop-down menu. The end-user can modify the query by merely clicking on the generated graph to ask related questions. This study showcases the potential of knowledge graphs centered on smell and taste disorders to organize and provide accessible health data to end-users.

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%.

With the introduction of smart phones, the marketing possibilities for businesses changed fundamentally. New advertisement and publication mechanisms developed a totally new way of communicating with customers more often and in a much more personalized way. This paper describes a design approach for the development and implementation of a tool which can be used by the hotel business as well as by other end-user oriented businesses. It helps to keep close contact with customers by using a technology almost everyone uses nowadays -- the smart phone. To accomplish the above mentioned requirements we propose the design and implementation of a content management system (CMS) rendering mobile apps for different platforms: Android, iOS and in mobile website mode. After the roll out of the resulting product and a testing phase with two customers it is apparent that the utilization of mobile marketing mechanisms really increases the brand touch points by an average of 17% and has a high acceptance rate by customers of all ages, genders and social environments.

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.

In the energy domain a transition process has begun aiming at the realization of smart grids, where many perspectives on smart grids and thus, different definitions, exist. However, one aspect is widely accepted, namely that ICT is needed to support future smart grids. IT approaches are used to deal with the upcoming data exchange among the different stakeholders. The traditional supply chain, from centralized generation to conventional consumption, has evolved into a multi-dimensional, highly dynamic and complex system. Many new stakeholders such as service providers and prosumers participate in smart grids. Also, automation becomes more and more important, especially novel communication standards like the OPC UA. In this contribution the application of the OPC UA as well as its extension by annotating meta-data to enable semantic web service communication is introduced.

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.

Today's wireless and mobile service platforms are typically monolithic and centralized in nature, and they do not support heterogeneous service access and the sharing of service usage experience. New sources of revenue for providers are expected to include tailored, personalized, and dynamically composed services that are fast to market, cost efficient, and provide compelling user experience. To meet the current market needs, the SPICE service delivery platform extends the conventional IMS by supporting advanced added-value services that are composed of more primitive services. We describe the IMS role and functions in SPICE, and the use of ontology and Semantic Web technologies for achieving a better knowledge management in mobile service platforms.

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.