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A RDF Schema that defines concepts and relationships common to all Open Government Data @en
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GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
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The General Data Protection Regulation (GDPR) is comprised of several articles, each with points that refer to specific concepts. The general convention of referring to these points and concepts is to quote the specific article or point using a human-readable reference. This ontology provides a way to refer to the points within the GDPR using the EurLex ontology published by the European Publication Office. It also defines the concepts defined, mentioned, and requried by the GDPR using the Simple Knowledge Organization System (SKOS) ontology. @en
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A simple RDF vocabulary containing terms to facilitate the linking of genealogical data. @en
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A vocabulary for representing latitude, longitude and altitude information in the WGS84 geodetic reference datum. @en
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Ontology defining concepts for Geocoding of addresses. It is based on the geocoding used in the Global Legal Entity Identifier Foundation (GLEIF) Golden Copy Data, but is more broadly applicable. @en
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Vocabulary describing the administrative subdivision of Norway @en
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This ontology contains geographic feature classes and associated properties including classes and properties for describing the spatial location of the geographic feature. The classes and properties have been defined based on an ESRI dataset. @en
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An ontology describing the administrative divisions in France. @en
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An ontology for describing the shape and the location of topographic entities @en
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FAO's geopolitical ontology version 1.1 was populated with FAO, UN and internationally recognized data sources. @en
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The GeoSpecies Ontology is used to describe geographical distribution of living species. @en
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An OWL representation of part of the General Feature Model described in ISO 19109 and the General Feature Instance model described in Annex C of ISO 19156:2011. @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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Ontology for legal entity identifier registration. It was designed for Global Legal Entity Identifier Foundation (GLEIF) Level 1 data corresponding to the Common Data Format version 2.1. It covers key reference data for a legal entity identifiable with an LEI. The ISO 17442 standard developed by the International Organization for Standardization defines a set of attributes or LEI reference data that comprises the most essential elements of identification. It specifies the minimum reference data, which must be supplied for each LEI: The official name of the legal entity as recorded in the official registers. The registered address of that legal entity. The country of formation. The codes for the representation of names of countries and their subdivisions. The date of the first LEI assignment; the date of last update of the LEI information; and the date of expiry, if applicable. @en