The Internet of Things (IoT) has revolutionized various applications including agriculture, but it still faces challenges in data collection and understanding. This paper proposes a real-time framework with three additional semantic layers to help IoT devices and sensors comprehend data meaning and source. The framework consists of six layers: perception, semantic annotation, interoperability, transportation, semantic reasoning, and application, suitable for dynamic environments. Sensors collect data in the form of voltage, which is then processed by microprocessors or microcontrollers in the semantic annotation and preprocessing layer. Metadata is added to the raw data, including the purpose, ID number, and application. Two semantic algorithms are proposed in the semantic interoperability and ontologies layer: the interoperability semantic algorithm for standardizing file types and the synonym identification algorithm for identifying synonyms. In the transportation layer, raw data and metadata are sent to other IoT devices or cloud computing platforms using techniques like WiFi, Zigbee networks, Bluetooth, and mobile communication networks. A semantic reasoning layer is proposed to infer new knowledge from the existing data, using fuzzy logic, Dempster-Shafer theory, and Bayesian networks. A Graphical User Interface (GUI) is proposed in the application layer to help users communicate with and monitor IoT sensors, devices, and new knowledge inferred. This framework provides a robust solution for managing IoT data, ensuring semantic completeness, and enabling real-time knowledge inference. The integration of uncertainty reasoning methods and semantic interoperability techniques makes this framework a valuable tool for advancing IoT applications in general and in agriculture in particular.

Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twin (DT) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). Digital twins facilitate monitoring and provide constant health insight to improve quality of life and well-being with better personalization. Healthcare DT challenges are standardizing data formats, communication protocols, and data exchange mechanisms. To achieve those data integration and knowledge challenges, we designed the Mental Health Knowledge Graph (ontology and dataset) to boost mental health. The Knowledge Graph (KG) acquires knowledge from ontology-based mental health projects classified within the LOV4IoT ontology catalog (Emotion, Depression, and Mental Health). Furthermore, the KG is mapped to standards (e.g., ontologies) when possible. Standards from ETSI SmartM2M, ITU/WHO, ISO, W3C, NIST, and IEEE are relevant to mental health.

This research introduces a novel approach for achieving semantic interoperability in digital twins and assisting the creation of Asset Administration Shell (AAS) as digital twin model within the context of Industry 4.0. The foundational idea of our research is that the communication based on semantics and the generation of meaningful textual data are directly linked, and we posit that these processes are equivalent if the exchanged information can be serialized in text form. Based on this, we construct a "semantic node" data structure in our research to capture the semantic essence of textual data. Then, a system powered by large language models is designed and implemented to process the "semantic node" and generate standardized digital twin models from raw textual data collected from datasheets describing technical assets. Our evaluation demonstrates an effective generation rate of 62-79%, indicating a substantial proportion of the information from the source text can be translated error-free to the target digital twin instance model with the generative capability of large language models. This result has a direct application in the context of Industry 4.0, and the designed system is implemented as a data model generation tool for reducing the manual effort in creating AAS model. In our evaluation, a comparative analysis of different LLMs and an in-depth ablation study of Retrieval-Augmented Generation (RAG) mechanisms provide insights into the effectiveness of LLM systems for interpreting technical concepts and translating data. Our findings emphasize LLMs' capability to automate AAS instance creation and contribute to the broader field of semantic interoperability for digital twins in industrial applications. The prototype implementation and evaluation results are presented on our GitHub Repository: https://github.com/YuchenXia/AASbyLLM.

Although the goal of achieving semantic interoperability of electronic health records (EHRs) is pursued by many researchers, it has not been accomplished yet. In this paper, we present a proposal that smoothes out the way toward the achievement of that goal. In particular, our study focuses on medical diagnoses statements. In summary, the main contributions of our ontology-based proposal are the following: first, it includes a canonical ontology whose EHR-related terms focus on semantic aspects. As a result, their descriptions are independent of languages and technology aspects used in different organizations to represent EHRs. Moreover, those terms are related to their corresponding codes in well-known medical terminologies. Second, it deals with modules that allow obtaining rich ontological representations of EHR information managed by proprietary models of health information systems. The features of one specific module are shown as reference. Third, it considers the necessary mapping axioms between ontological terms enhanced with so-called path mappings. This feature smoothes out structural differences between heterogeneous EHR representations, allowing proper alignment of information.

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.

This article presents the methodology that has been successfully used over the past seven years by an interdisciplinary team to create the International Committee for Documentation of the International Council of Museums (CIDOC) CONCEPTUAL REFERENCE MODEL (CRM), a high-level ontology to enable information integration for cultural heritage data and their correlation with library and archive information. The CIDOC CRM is now in the process to become an International Organization for Standardization (ISO) standard. This article justifies in detail the methodology and design by functional requirements and gives examples of its contents. The CIDOC CRM analyzes the common conceptualizations behind data and metadata structures to support data transformation, mediation, and merging. It is argued that such ontologies are propertycentric, in contrast to terminological systems, and should be built with different methodologies.

Semantic interoperability includes the ability to establish a shared meaning of the data exchanged, as well as the ability to similarly interpret communication interfaces. Shared meaning here means that two different computer systems, for example, not only can communicate data in the basic sense (such as an integer with value 42), but also attach unambiguous meaning to the data. For example, radiator three's temperature in the conference room on level five is currently 42 Celsius. As we build large IoT systems we are faced with several challenges of scale. Among them is the challenge of being able to make equipment and subsystems of different vendors interoperable and, over different time periods, work together and as intended.

The Virtual Materials Marketplace (VIMMP) project, which develops an open platform for providing and accessing services related to materials modelling, is presented with a focus on its ontology development and data technology aspects. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology (EMMO), which is based on mereotopology following Varzi and semiotics following Peirce, is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace frontend.

When I think about "managing information" and using "information of many types and from many sources" I think about the different levels of interoperability of that information and the different types of AI and automation that occurs at different levels of interoperability. In this article, I introduce 4 levels of interoperability used in industries like Healthcare and the associated AI and automation that aligns with or is enabled by increasing levels of interoperability. These 4 levels of interoperability are critical to managing information and realizing the full potential of AI and automation for enabling a "holistic cyber defense machine". Foundational Interoperability (Level 1) – establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another. Foundational Interoperability lets the data transmitted by one system to be received by another.

Interoperability, defined dryly, is the seamless, secure and controlled exchange of data between different applications and technologies. The term is a mouthful, yet the concept enables many conveniences we take for granted. In education, however, interoperability has lagged behind other industries and services. Data is fragmented across different systems that don't "speak" to one another. This means records are not easily transferable between tools used within the same school or district.