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Defines properties and relationships between works. @en
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The Translational Medicine Ontology (TMO) is a high-level, patient-centric ontology that extends existing domain ontologies to integrate data across aspects of drug discovery and clinical practice. The ontology has been developed by participants in the World Wide Web Consortium's Semantic Web for Health Care and Life Sciences Interest Group @en
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An RDF vocabulary for relating SW vocabulary terms to their status. @en
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Ontology for Relating Generic and Specific Information Resources @en
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ADMS is a profile of DCAT, used to describe semantic assets (or just 'Assets'), defined as highly reusable metadata (e.g. xml schemata, generic data models) and reference data (e.g. code lists, taxonomies, dictionaries, vocabularies) that are used for eGovernment system development. @en
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Defines the element of Authorization and its essential properties, and also some classes of access such as read and write. @en
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A model for the representation of lexical information relative to ontologies. Variation and translation module. @en
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The Ontology for Media Resources 1.0 describes a core vocabulary of properties and a set of mappings between different metadata formats of media resources hat describe media resources published on the Web (as opposed to local archives, museums, or other non-web related and non-shared collections of media resources). @en
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A vocabulary which can be used to specify a mapping of relational data to RDF. @en
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The Registered Organization Vocabulary is a profile of the Organization Ontology for describing organizations that have gained legal entity status through a formal registration process, typically in a national or regional register. @en
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ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments @en
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This ontology is based on the SSN Ontology by the W3C Semantic Sensor Networks Incubator Group (SSN-XG), together with considerations from the W3C/OGC Spatial Data on the Web Working Group. @en
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The GLACIATION platform develops a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture to reduce energy consumption, improving data processing and optimizing data movement operations. Towards this aim, the platform needs to consume the data and metadata that are fed into the DKG. The metadata can affect and inform the decision-making processes in the GLACIATION architecture and introduces the GLACIATION Metadata Reference Model that will be used for modelling the metadata in the DKG. The GLACIATION Reference Metadata Model makes data ingestion and processing interoperable inside the GLACIATION platform. Linked Data allows for a high level of flexibility and to tackle the variety and merging issues that emerge in heterogenous environments, especially due to the wide range of sensors and other data sources that the platform may integrate. The GLACIATION Reference Metadata Model is tailored to fit the specific purposes of the GLACIATION platform, while the GLACIATION use cases define the scope of the model for better interoperability. There are common metadata challenges for all use cases. This stems from the use of the Kubernetes orchestration system as a basis for the GLACIATION platform. In addition, common to the platform is the ingestion of data from other sources into the DKG that can then be used to affect processing decisions. There are direct data flows from sensors within the system, but also data and metadata from sources external to the system. This allows the system to react e.g. to environmental situations like weather or temperature, but also to requirements concerning security or privacy. Exemplary uses and specializations of the reference model to the GLACIATION use cases are also provided. The GLACIATION Metadata Reference Model can be used for scheduling and performing tasks. The model can be considered as a general conceptualization of a tasks scheduling problem that considers various measuring indicators over the deployed resources. It captures the assignment of time-constrained tasks to time constrained and energy consuming resources, that can satisfy various hard and soft constraints, even compositions of such constraints. The tasks can be monitored through various measuring resources using a variety of single or aggregated, predicted or real measurements. The model is generic, by being both domain and application independent, describing the scheduling tasks, without providing specific solutions on how they can be solved. It can be easily adjusted to each of the current three GLACIATION use cases, covering also the Kubernetes orchestration and its Telemetry System deployed by the project. The proposed model makes it feasible to answer the competency queries defined by each of the Glaciation's use case. @en
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This ontology models trust recommendation concepts in SIoT to bridge the gap between abstract trust concepts and real-world device concepts. @en
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The ontology describes the main concepts in the field of education and the connections between them. The current version emphasizes the details of the study material, learning outcomes and the curriculum. @en