The registered version of this article, first published in IEEE Internet of Things Journal, is available online at the publisher's website: Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/JIOT.2026.3696106

Deep neural networks excel in vision and language but often struggle with tabular data due to weak inductive biases and limited interpretability. While converting tabular data into synthetic images enables Convolutional Neural Networks (CNNs) to exploit spatial feature interactions, this approach typically exacerbates the models’ black-box nature. To achieve both high predictive power and transparent decision-making, we propose an interpretable Hybrid Neural Network that integrates visual reasoning (CNNs applied to synthetic images) with symbolic learning (Kolmogorov-Arnold Networks (KAN) applied to tabular data). Extensive evaluations across diverse datasets demonstrate that our CNN–KAN hybrids achieve highly competitive, and often superior, predictive performance. To audit their internal reasoning, we introduce the Global Feature Score (GFS), a unified metric that aggregates spatial saliency from Grad-CAM with symbolic attributions from the KAN branch, producing consistent feature relevance rankings. By explicitly diagnosing modality dominance, layout artifact sensitivity, and inter-branch agreement, the proposed framework significantly reduces reliance on fragile post-hoc methods. Ultimately, embedding interpretability directly into the architecture provides auditable, cross-verified explanations, establishing a robust foundation for Responsible AI-oriented tabular learning.

From centralised platforms to decentralised ecosystems, like Data Spaces, sharing data has become a paramount challenge. For this reason, the definition of data usage policies has become crucial in these domains, highlighting the necessity of effective policy enforcement mechanisms. The Open Digital Rights Language (ODRL) is a W3C standard ontology designed to describe data usage policies, however, it lacks built-in enforcement capabilities, limiting its practical application. This paper introduces the Open Digital Rights Enforcement (ODRE) framework, whose goal is to provide ODRL with enforcement capabilities. The ODRE framework proposes a novel approach to express ODRL policies that integrates the descriptive ontology terms of ODRL with other languages that allow behaviour specification, such as dynamic data handling or function evaluation. The framework includes an enforcement algorithm for ODRL policies and two open-source implementations in Python and Java. The ODRE framework is also designed to support future extensions of ODRL to specific domain scenarios. In addition, current limitations of ODRE, ODRL, and current challenges are reported. Finally, to demonstrate the enforcement capabilities of the implementations, their performance, and their extensibility features, several experiments have been carried out with positive results.

ABSTRACT Recommender systems are tools for interacting with large and complex information spaces by providing a personalised view of such spaces, prioritising items that are likely to be of interest to the user. In addition, they serve as a significant tool in academic research, helping authors select the most appropriate journals for their academic articles. This paper presents a comprehensive study of various journal recommender systems, focusing on the synergy of graph neural networks (GNNs) with pretrained transformers for enhanced text classification. Furthermore, we propose a content‐based journal recommender system that combines a pretrained Transformer with a Graph Attention Network (GAT) using title, abstract and keywords as input data. The proposed architecture enhances text representation by forming graphs from the Transformers' hidden states and attention matrices, excluding padding tokens. Our findings highlight that this integration improves the accuracy of the journal recommendations and reduces the transformer oversmoothing problem, with RoBERTa outperforming BERT models. Furthermore, excluding padding tokens from graph construction reduces training time by 8%–15%. Furthermore, we offer a publicly available dataset comprising 830,978 articles.

In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.

In recent years, the development of Natural Language Processing (NLP) text-to-face encoders and Generative Adversarial Networks (GANs) has enabled the synthesis and generation of facial images from textual description. However, most encoders have been developed for the English language. This work presents the first study of three text-to-face encoders, namely, the RoBERTa pre-trained model and the Sent2Vec and RoBERTa models, trained with the CelebA dataset in Spanish. It then introduces customised and fine-tuned conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) trained with the CelebA dataset for text-to-face generation in Spanish. To validate the results obtained, a qualitative evaluation was carried out with a visual analysis and a quantitative evaluation based on the IS, FID and LPIPS metrics. Our findings show promising results with respect to the literature, improving the numerical metrics of FID and LPIPS by 5% and 37%, respectively. Our results also show, through a quantitative–qualitative comparison of the cDCGAN training epochs, that the IS metric is not a reliable objective metric to be considered in the evaluation of similar works.

Demand Response (DR) is becoming a cornerstone element in the current energy sector, particularly for the EU energy markets. For this reason, considerable effort has been spent on standardising demand response data models. As a result, there is an ever-growing number of demand response proposals based on these standards. However, these proposals are usually centralised, and those that rely on cloud solutions use the cloud as a centralised data store assuming that the data is already homogenised when stored, i.e. all the data has the same format and model. Nevertheless, in practise, DR proposals rely on several components that provide data in heterogeneous formats and models. Furthermore, the different DR standards define models for different data formats that hinder data exchange between different DR systems. In this article, a generic tool called CIM is presented, which allows existing DR systems to distribute their components in the cloud, providing a solid security and privacy framework for data exchange. In addition, the CIM implements a semantic interoperability layer that is capable of translating data into a normalised form when exchanged so that it can be transparently consumed by DR components. Experiments advocate the CIM as a solution for DR systems to decentralise their architectures and exchange heterogeneous data even with other DR systems that follow different DR standards.

Ontology Engineering has captured much attention during the last decades leading to the proliferation of numerous works regarding methodologies, guidelines, tools, resources, etc. including topics which are still being investigated. Even though, there are still many open questions when addressing a new ontology development project, regarding how to manage the overall project and articulate transitions between activities or which tasks and tools are recommended for each step. In this work we propose the Linked Open Terms (LOT) methodology, an overall and lightweight methodology for building ontologies based on existing methodologies and oriented to semantic web developments and technologies. The LOT methodology focuses on the alignment with industrial development, in addition to academic and research projects, and software development, that is making ontology development part of the software industry. This methodology includes lessons learnt from more than 20 years in ontological engineering and its application on 18 projects is reported.

The increasing interest of demand response (DR) strategies has fostered the funding of new research projects in Europe. The development of DR in these projects requires a bi-directional data exchange, which revolves around diverse overlapping topics, between infrastructures and providers. As a result, ontologies are required to enable a transparent exchange and consumption of data, ensuring its semantic correctness. Nevertheless, currently, there is no ontology that can be used for such data exchange covering all these topics. In the context of the EU project DELTA, which focuses on DR, an ontology has been developed with the goal of filling the aforementioned lack. This paper describes the approach followed to develop this ontology, specifies its requirements, and also includes a practical example of how to use it.

Knowledge Graphs (KGs) that publish RDF data modelled using ontologies in a wide range of domains have populated the Web. The SHACL language is a W3C recommendation that has been endowed to encode a set of either value or model data restrictions that aim at validating KG data, ensuring data quality. Developing shapes is a complex and time consuming task that is not feasible to achieve manually. This article presents two resources that aim at generating automatically SHACL shapes for a set of ontologies: (1) Astrea-KG, a KG that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraint patterns, and (2) Astrea, a tool that automatically generates SHACL shapes from a set of ontologies by executing the mappings from the Astrea-KG. These two resources are openly available at Zenodo, GitHub, and a web application. In contrast to other proposals, these resources cover a large number of SHACL restrictions producing both value and model data restrictions, whereas other proposals consider only a limited number of restrictions or focus only on value or model restrictions.

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile computing; learning (artificial intelligence); autonomous aerial vehicles; protocols; computer network security; data privacy; data analysis; telecommunication traffic.

Ontological requirements play a key role in ontology development as they determine the knowledge that needs to be modelled. In addition, the analysis of such requirements can be used (a) to improve ontology testing by easing the automation of requirements into tests; (b) to improve the requirements specification activity; or (c) to ease ontology reuse by facilitating the identification of patterns. However, there is a lack of openly available ontological requirements published together with their associated ontologies, which hinders such analysis. Therefore, in this work we present CORAL (Corpus of Ontological Requirements Annotated with Lexico-syntactic patterns), an openly available corpus of 834 ontological requirements annotated and 29 lexico-syntactic patterns, from which 12 are proposed in this work. CORAL is openly available in three different open formats, namely, HTML, CSV and RDF under “Creative Commons Attribution 4.0 International” license.

The validation of ontologies, whose aim is to check whether an ontology matches the conceptualization it is meant to specify, is a key activity for guaranteeing the quality of ontologies.This work is focused on the validation through requirements, with the aim of assuring, both the domain experts and ontology developers, that the ontologies they are building or using are complete regarding their needs.Inspired by software engineering testing processes, this work proposes a web-based tool called Themis, independent of any ontology development environment, for validating ontologies by means of the application of test expressions which, following lexicosyntactic patterns, represent the desired behaviour that will present an ontology if a requirement is satisfied.

The aim of the proposed work is to introduce a secure and interoperable Demand Response (DR) management platform that will assist Aggregators (or other relevant Stakeholders involved in DR business scenarios) in their decision making mechanisms over their portfolios of prosumers. This novel architecture incorporates multiple strategies and policies provided from energy market stakeholders, establishing a more modular and future-proof DR solution. By employing an innovative multi-agent decision making system and self-learning algorithms to enable aggregation, segmentation and coordination of several diverse clusters, consisting of supply and demand assets, a fully autonomous design will be delivered. This DR framework is further fortified in terms of data security by not only implementing cutting-edge blockchain infrastructure, but also by making use of Smart Contracts and Decentralized Applications (dApps) which will further secure and facilitate Aggregators-to-Prosumers transactions. The blockchain technologies will be combined with well-known open protocols (i.e. OpenADR) towards also supporting interoperability in terms of information exchange.

Terminologies in the biomedical field are one of the main resources used in the clinical practice. Keeping them up-to-date to meet real-world use cases is a critical operation that even in the case of well maintained terminologies such as SNOMED-CT involves much effort from domain experts. Pharmacological products or drugs are constantly being approved and made available in the market and their clinical information should be also updated in terminologies. Each new drug is provided with its Summary of Product Characteristics (SPC), a document in natural language that contains its essential information.

Knowledge Graphs (KG) are becoming core components of most artificial intelligence applications. Linked Data, as a method of publishing KGs, allows applications to traverse within, and even out of, the graph thanks to global dereferenceable identifiers denoting entities, in the form of IRIs. However, as we show in this work, after analyzing several popular datasets (namely DBpedia, LOD Cache, and Web Data Commons JSON-LD data) many entities are being represented using literal strings where IRIs should be used, diminishing the advantages of using Linked Data. To remedy this, we propose an approach for identifying such strings and replacing them with their corresponding entity IRIs. The proposed approach is based on identifying relations between entities based on both ontological axioms as well as data profiling information and converting strings to entity IRIs based on the types of entities linked by each relation. Our approach showed 98% recall and 76% precision in identifying such strings and 97% precision in converting them to their corresponding IRI in the considered KG. Further, we analyzed how the connectivity of the KG is increased when new relevant links are added to the entities as a result of our method. Our experiments on a subset of the Spanish DBpedia data show that it could add 25% more links to the KG and improve the overall connectivity by 17%.

Privacy protection in published data sets is of crucial importance, and anonymisation is one well-known technique for privacy protection that has been successfully used in practice.However, existing anonymisation frameworks have in mind specific data structures (i.e., tabular data) and, because of this, these frameworks are difficult to apply in the case of RDF data.This paper presents an RDF anonymisation framework that has been developed to address the particularities of the RDF specification.Such framework includes an anonymisation model for RDF data, a set of anonymisation operations for the implementation of such model, and a metric for measuring precision and distortion of anonymised RDF data.Furthermore, this paper presents a use case of the proposed RDF anonymisation framework.

Current solutions to the interoperability problem in Home Automation systems are based on a priori agreements where protocols are standardized and later integrated through specific gateways. In this regards, spontaneous interoperability, or the ability to integrate new devices into the system with minimum planning in advance, is still considered a major challenge that requires new models of connectivity. In this paper we present an ontology-driven communication architecture whose main contribution is that it facilitates spontaneous interoperability at system model level by means of semantic integration. The architecture has been validated through a prototype and the main challenges for achieving complete spontaneous interoperability are also evaluated.

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

Interoperability among different development tools is not a straightforward task since ontology editors rely on specific internal knowledge models which are translated into common formats such as RDF(S). This paper addresses the urgent need for interoperability by providing an exhaustive set of benchmark suites for evaluating RDF(S) import, export and interoperability. It also demonstrates, in an extensive field study, the state-of-the-art of interoperability among six Semantic Web tools. From this field study we have compiled a comprehensive set of practices that may serve as recommendations for Semantic Web tool developers and ontology engineers.