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The AMLO-core is the main module of the AMLO projects that extends the Financial Industry Business Ontology (FIBO) with some concepts to describe the Anti Money Laundering (AML) knowledge and facts. @en
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The generic BBC ontology for people, places,events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts @en
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Lemon: The lexicon model for ontologies is designed to allow for descriptions of lexical information regarding ontological elements and other RDF resources. Lemon covers mapping of lexical decomposition, phrase structure, syntax, variation, morphology, and lexicon-ontology mapping. @en
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The participation ontology is a simple model for describing the roles that people play within groups. It is intended that specific domains will create subclasses of roles within their own areas of expertise. @en
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The objective of SAREF4EHAW is to extend SAREF ontology for the eHealth/Ageing-well (EHAW) vertical. Clause 4.1 of the present document shortly introduces a high level view of the envisioned SAREF4EHAW semantic model and modular ontology, with the retained concepts (i.e. classes) and their relations. SAREF4EHAW extension has been specified and formalised by investigating EHAW domain related resources, as reported in ETSI TR 103 509, such as: potential stakeholders, standardization initiatives, alliances/associations, European projects, EC directives, existing ontologies, and data repositories. Therefore, SAREF4EHAW modular ontology shall both: - Allow the implementation of a limited set of typical EHAW related use cases already identified in ETSI TR 103 509, i.e. - Use case 1 ?elderly at home monitoring and support?, - Use case 2 ?monitoring and support of healthy lifestyles for citizens?, - Use case 3 ?Early Warning System (EWS) and Cardiovascular Accidents detection?. - Fulfil the eHealth Ageing Well related requirements provided in ETSI TR 103 509, mainly the ontological ones that were mostly taken as input for the ontology specification. SAREF4EHAW mainly reuses the following existing ontologies: SAREF, ETSI SmartBAN reference model, SAREF 4 Environment extension and W3C SSN System module. The following figure presents the high level view of SAREF4EHAW ontology.  For semantic interoperability handling purposes, an ontology based solution, combined with sensing-as-a-service and WoT strategies, is retained for SAREF4EHAW. Therefore, an upper level ontology, at service level, shall also behas been fully modelled (Service class and sub-classes depicted in the previous figure). For embedded semantic analytics purposes, SAREF4EHAW shall behas been designed using the modularity principle (see ETSI TR 103 509) and can thus be mainly described by the following self-contained knowledge sub-ontologies (or modules): HealthActor, Ban, HealthDevice, Function (measured data related concepts included) and Service. @en
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The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @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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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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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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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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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