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This is an ontology representation of the generic features defined by Climate and Forecast (CF) standard names vocabulary (http://cf-pcmdi.llnl.gov/documents/cf-standard-names/), maintained by the Program for Climate Model Diagnosis and Intercomparison (http://cf-pcmdi.llnl.gov/ ) which is intended for use with climate and forecast data, in the atmosphere, surface and ocean domains. @en
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The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the World Wide Web. This ontology is a non-normative OWL formalization of the textual OA specification at http://www.openannotation.org/spec/core/20130208/index.html @en
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This ontology is a translation of the General Transit Feed Specification towards URIs. Its intended use is creating an exchange platform where the Linked GTFS model can be used as a start to get the right data into the right format. @en
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This is the human and machine readable Vocabulary/Ontology governed by the European Union Agency for Railways. It represents the concepts and relationships linked to the sectorial legal framework and the use cases under the Agency´s remit. Currently, this vocabulary covers the European railway infrastructure and the vehicles authorized to operate over it. It is a semantic/browsable representation of the RINF and ERATV application guides that were built by domain experts in the RINF and ERATV working parties. Since version 2.6.0, the ontology includes the routebook concepts described in appendix D2 \"Elements the infrastructure manager has to provide to the railway undertaking for the Route Book\" (https://eur-lex.europa.eu/eli/reg_impl/2019/773/oj) and the appendix D3 \"ERTMS trackside engineering information relevant to operation that the infrastructure manager shall provide to the railway undertaking\". @en
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The DNB RDF Vocabulary (dnb:) is a collection of classes, properties and datatypes used within the DNB's linked data service.It complements the GND Ontology (gndo:) which is specifically geared towards authority data from the Integrated Authority File (GND), whereas this vocabulary is more general-purpose. @en
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ProVoc (Product Vocabulary) is a vocabulary that can be used to represent information and manipulate them through the Web. This ontology reflects: 1) The basic hierarchy of a company: Group (Company), Divisions of a Group, Brand names attached to a Division or a Group, and 2) The production of a company: products, ranges of products (attached to a Brand), the composition of a product, packages of products... @en
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The VAS ontological model enables the semantic integration of the heterogeneous observations used in ASOTVAS project ( https://robotica.uv.es/proyectos/ASOTVAS/ ), including ground measurements, UAV acquisitions and satellite products. Built as an extension of the W3C SOSA ontology (Janowicz et al., 2018), it incorporates a domain-specific vocabulary tailored to the needs of the Valencia Anchor Station as a CEOS LPV supersite. The model provides additional classes and properties to represent, in a homogeneous way, the different observational platforms: field sensors installed at VAS stations, UAVs equipped with multispectral cameras, and satellite missions such as Sentinel-2 and Sentinel-3. All observations follow a common SOSA pattern and share the same structure for results, units and timestamps. By aligning field, UAV and satellite observations under a unified semantic framework, the VAS ontology supports interoperable data access, consistent representation across scales, and integrated analysis of the multi-source measurements collected in ASOTVAS. @en
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ISO 37120 – Sustainable Development and Resilience of Communities – Indicators for City Services and Quality of Life (under TC268) http://ontology.eil.utoronto.ca/ISO37120.html This OWL file defines a class for each indicator defined in the ISO 37120 standard. Names for each indicator are provided. Text definitions are provided only for Economy, Education and Energy indicators, due to copyright restrictions imposed by ISO. This file is meant to provide a single URI for each indicator. An ontology for representing an indicator's supporting data plus meta information such as provenance, validity and trust can be found in: http://ontology.eil.utoronto.ca/GCI/Foundation/GCI-Foundation.owl Documentation of the ontology can be found in: http://eil.utoronto.ca/smartcities/papers/GCI-Foundation-Ontology.pdf @en
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The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
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The present document is the technical specification of SAREF4SYST, a generic extension of [ETSI TS 103 264 SAREF](https://www.etsi.org/deliver/etsi_ts/103200_103299/103264/02.01.01_60/ts_103264v020101p.pdf) that defines an ontology pattern which can be instantiated for different domains. SAREF4SYST defines Systems, Connections between systems, and Connection Points at which systems may be connected. These core concepts can be used generically to define the topology of features of interest, and can be specialized for multiple domains. The topology of features of interest is highly important in many use cases. If a room holds a lighting device, and if it is adjacent with an open window to a room whose luminosity is low, then by turning on the lighting device in the former room one may expect that the luminosity in the latter room will rise. The SAREF4SYST ontology pattern can be instantiated for different domains. For example to describe zones inside a building (systems), that share a frontier (connections). Properties of systems are typically state variables (e.g. agent population, temperature), whereas properties of connections are typically flows (e.g. heat flow). SAREF4SYST has two main aims: on the one hand, to extend SAREF with the capability or representing general topology of systems and how they are connected or interact and, on the other hand, to exemplify how ontology patterns may help to ensure an homogeneous structure of the overall SAREF ontology and speed up the development of extensions. SAREF4SYST consists both of a core ontology, and guidelines to create ontologies following the SAREF4SYST ontology pattern. The core ontology is a lightweight OWL-DL ontology that defines 3 classes and 9 object properties. Use cases for ontology patterns are described extensively in [ETSI TR 103 549 Clauses 4.2 and 4.3](https://www.etsi.org/deliver/etsi_tr/103500_103599/103549/01.01.01_60/tr_103549v010101p.pdf). For the Smart Energy domain: - Electric power systems can exchange electricity with other electric power systems. The electric energy can flow both ways in some cases (from the Public Grid to a Prosumer), or in only one way (from the Public Grid to a Load). Electric power systems can be made up of different sub-systems. Generic sub-types of electric power systems include producers, consumers, storage systems, transmission systems. - Electric power systems may be connected one to another through electrical connection points. An Electric power system may have multiple connection points (Multiple Winding Transformer generally have one single primary winding with two or more secondary windings). Generic sub-types of electrical connection points include plugs, sockets, direct-current, single-phase, three-phase, connection points. - An Electrical connection may exist between two Electric power systems at two of their respective connection points. Generic sub-types of electrical connections include Single-phase Buses, Three-phase Buses. A single-phase electric power system can be connected using different configurations at a three-phase bus (RN, SN, TN types). For the Smart Building domain: - Buildings, Storeys, Spaces, are different sub-types of Zones. Zones can contain sub-zones. Zones can be adjacent or intersect with other zones. - Two zones may share one or more connections. For example some fresh air may be created inside a storey if it has two controllable openings to the exterior at different cardinal points. A graphical overview of the SAREF4SYST ontology is provided in Figure 1. In such figure: - Rectangles are used to denote Classes. The label of the rectangle is the identifier of the Class. - Plain arrows are used to represent Object Properties between Classes. The label of the arrow is the identifier of the Object Property. The origin of the arrow is the domain Class of the property, and the target of the arrow is the range Class of the property. - Dashed arrows with identifiers between stereotype signs (i.e. "`<< >>`") refer to OWL axioms that are applied to some property. Four pairs of properties are inverse one of the other; the property `s4syst:connectedTo` is symmetric, and properties `s4syst:hasSubSystem` and `s4syst:hasSubSystem` are transitive. - A symbol =1 near the target of an arrow denotes that the associated property is functional. A symbol ? denotes a local existential restriction.  @en
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A vocabulary, or music ontology, to describe classical music and performances. Classes (categories) for musical works, events, instruments and performers, as well as related properties are defined. Make sure to distinguish musical works (e.g. Opera) from performance events (Opera_Event), or works (String_Quartette) from performer (StringQuartetEnsemble in this vocab), whose natural language terms are used interchangeblly. The present version experiments more precise model to describe a musical work, its representations (performances, scores, etc) and a musical event to present a representation (a concert). Includes 30 keys as individuals. @en
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IoT-O is a core domain Internet of Things ontology. It is intended to model horizontal knowledge about IoT systems and applications, and to be extended with vertical, application specific knowledge. It is constituted of different modules : - A sensing module, based on W3C's SSN (http://purl.oclc.org/NET/ssnx/ssn) - An acting module, based on SAN (http://www.irit.fr/recherches/MELODI/ontologies/SAN) - A service module, based on MSM (http://iserve.kmi.open.ac.uk/ns/msm/msm-2014-09-03.rdf) and hRest (http://www.wsmo.org/ns/hrests) - A lifecycle module, based on a lifecycle vocabulary (http://vocab.org/lifecycle/schema-20080603.rdf) and an iot-specific extension (http://www.irit.fr/recherches/MELODI/ontologies/IoT-Lifecycle) - An energy module, based on powerOnt (ttp://elite.polito.it/ontologies/poweront.owl) IoT-O developping team also contributes to the oneM2M IoT interoperability standard. @en
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SAREF4INMA is an extension of SAREF for the industry and manufacturing domain. SAREF4INMA focuses on extending SAREF for the industry and manufacturing domain to solve the lack of interoperability between various types of production equipment that produce items in a factory and, once outside the factory, between different organizations in the value chain to uniquely track back the produced items to the corresponding production equipment, batches, material and precise time in which they were manufactured. SAREF4INMA is specified and published by ETSI in the TS 103 410-5 associated to this ontology file. SAREF4INMA was created to be aligned with related initiatives in the smart industry and manufacturing domain in terms of modelling and standardization, such as the Reference Architecture Model for Industry 4.0 (RAMI), which combines several standards used by the various national initiatives in Europe that support digitalization in manufacturing. The full list of use cases, standards and requirements that guided the creation of SAREF4INMA are described in the associated ETSI TR 103 507. @en