In the rapidly evolving landscape of engineering education, upskilling and lifelong learning have become critical to maintaining competitiveness and fostering innovation. The use of ontologies, such as the European Skills, Competences, Qualifications, and Occupations (ESCO), plays a crucial role in organizing and managing the skills required for modern engineering roles. However, the slow pace of ontology updates and the lack of contextual adaptability present significant challenges, leading to outdated and irrelevant information for educators, learners, and industry professionals. This paper explores the potential of integrating Large Language Models (LLMs) with knowledge engineering to accelerate the process of updating ontologies like ESCO. By dynamically analyzing data and incor-porating contextual information, LLMs offer promising avenues for enhancing the evolution and precision of these ontologies. We discuss the potential impact of this approach in engineering education, particularly in aligning ups killing and reskilling efforts with the demands of emerging technologies such as AI -driven automation and digital engineering. This paper aims to highlight how LLMs can support the creation of more responsive, context-aware learning frameworks, ultimately sustaining educational ex-cellence and fostering critical thinking in engineering education.

Nowadays, Information Systems (IS) in healthcare systems are being successively updated, either incorporating advanced Optimization tools combined with Artificial Intelligence (AI) and Large Language Models (LLM), or using innovative computing features for storage, processing, security, privacy, and friendly interface. Large efforts are required to design, validate and implement such developments, diverse communities in multiple scientific domains are involved, and new approaches are key to cope with the transdisciplinary gaps. In this paper we outline and discuss the relevant technologies and methodologies for IS within the context of health emergencies. Advanced and innovative approaches are being applied within the Health Informatics area, and we address a set of key technologies (e.g., Cloud Computing, AI-based platforms, Neural Networks) and methodologies (e.g., Knowledge Graphs, advanced Optimization, Multi-Criteria Decision Making) that are being relevant within the Health Emergency Responses (HER) framework, and also for the health informatics communities.

Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources and hardly accessible by humans and machines. This paper evaluates current practices for publishing/accessing information on hazardous chemicals and proposes a novel platform designed to facilitate retrieval of critical chemical data in urgent situations. The platform aggregates information from multiple sources and organizes it into a structured knowledge graph. Users can access this information through a visual interface such as Neo4J Bloom and dashboards, or via natural language queries using a Chatbot. Our findings demonstrate a significant reduction in the time and effort required to access vital chemical information when datasets follow FAIR principles. Furthermore, we discuss the lessons learned from the development and implementation of this platform and provide recommendations for data owners and publishers to enhance data reuse and interoperability. This work aims to improve the accessibility and usability of chemical information by healthcare professionals, thereby supporting better health outcomes and informed decision-making in the face of patients exposed to chemical intoxication risks.

A radiological study is comprised by a set of images together with a medical report, which is generated by radiologists to describe the main characteristics of such images and the associated clinical findings. The radiological study provides relevant information about the patient's health condition which is necessary for physicians to accomplish diagnosis. However, some works demonstrated that the reports can be vague, incomplete, ambiguous or having inaccuracies, inconsistencies or errors. In this paper, we propose to use ontologies to support the elaboration of radiological reports. We propose an ontology to represent the medical knowledge about cranial computed tomography (CCT), which is among the most required radiological studies in emergency or regular treatments. Finally, we evaluate the quality of the reports generated based on this ontology.

One intrinsic characteristic of knowledge bases (KB), especially those published on the Web of data, is the frequent evolution of their data. Hence, changes that occur may lead to KB inconsistency and therefore, may generate contradictions between the KB facts and the KB axioms. In this paper, we propose an approach that is able to, first, compute and semantically represent the symmetric difference (diff) between two different versions of a KB and, second, use the generated diff to detect changes (addition and deletion) for the corresponding KB axioms. We further propose an experimental assessment of the approach on exisitng knowledge bases such as DBpedia.

Advances in the Web and healthcare data capture technologies have far-reaching benefits for the development of new clinical decision support systems that accelerate decisionmaking and generate personalized treatments.However, the diversity of healthcare data formats, the lack of computer interpretable representation of medical interventions, and the distribution of reliable medical knowledge sources constitute important barriers to better support the medical decision process.To deal with these issues, we propose the Treatment Plan Ontology (TPO) that formalizes medical interventions, and allows medical systems sharing and reasoning over them.This knowledge together with the acquired patient data are then reused by the autonomic processes that we have developed in order to timely detect anomalies and support the physicians in personalizing the patient treatment at the right time.We demonstrate the system efficiency through a use case for managing hyperglycemia in type 2 diabetes.

The 2014 edition of the Web2Touch (W2T) Track aims at presenting alternatives to address interoperability and distributed knowledge management problem for Web systems. In particular, it provides a view on the field of secure and knowledge-intensive Web collaboration using semantic techniques to share experiences, challenges and opportunities, and to cope with frequent evolution of knowledge during cooperative processes based on the Web collaborative platform. Consequently, this year, W2T brings together applications, engineering issues, conceptual models and methods to provide a multidisciplinary view over knowledge organization systems based on the Web.

Ontologies, or more generally speaking, Knowledge Organization Systems (KOS) have been developed to support the correct interpretation of shared data in collaborative applications. The quantity and the heterogeneity of domain knowledge often require several KOS to describe their content. In order to assure unambiguous interpretation, overlapped concepts of different, but domain-related KOS are semantically connected via mappings. However, in various domains, KOS periodically evolve creating the necessity of reviewing the validity of associated mappings. The size of KOS remains a barrier for a manual review of mappings, and rather requires the support of (semi-) automatic solutions. This article describes our experiences in understanding how KOS evolution affects mappings. We present our lessons learned from various empirical experiments, and we derive primary elements and requirements for improving the automation of mapping maintenance.

The highly dynamic nature of domain ontologies has a direct impact on semantic mappings established between concepts from different ontologies. Mappings must therefore be maintained according to ongoing ontology changes. Since many software applications exploit mappings for managing information and knowledge, it is important to define appropriate adaptation strategies to apply to existing mappings in order to keep their validity over time. In this article, we propose a set of mapping adaptation actions and present how they are used to maintain mappings up-to-date based on ontology change operations of different nature. We conduct an experimental evaluation using life sciences ontologies and mappings. We measure the evolution of mappings based on the proposed approach to mapping adaptation. The results confirm that mappings must be individually adapted according to the different types of ontology change.

Computer-Interpretable Guidelines (CIG) are Clinical Guidelines described in a language that can be interpreted by computers. They are often used to support physicians in a single point in time to design and test guidelines. The next steps are the application of CIGs to determine careflows, the personalization of careflows and their execution. This paper presents the iCareflow ontological framework which intends to support the adaptation of guidelines during the design phase in order to obtain personalized careflows. The proposed framework explores how guidelines, policies of the care institution, patient preferences, terminologies and other knowledge sources interact with each other to determine a patient-centric careflow. Scenarios and further applications of the framework are also provided.