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  • atts - Air Traffic Temporal and Spacial Vocabulary
    https://data.nasa.gov/ontologies/atmonto/general#
    Defines temporal / spatial concepts and general-purpose datastructures @en
  • rma - Verisav RMA / Ticketing Vocabulary
    https://ns.verisav.fr/rma#
    Vocabulary for Return Merchandise Authorization (RMA) and service ticket management, standardizing communication between consumers, retailers, and repairers. @en
  • dpp - Verisav Digital Product Passport Vocabulary
    https://ns.verisav.fr/dpp#
    Vocabulary for Digital Product Passports (DPP) managing product lifecycle, warranties, repairs, and compliance with EU regulations (ESPR EU 2024/1781). Aligned with GS1 Digital Link standards, including GTIN, GLN, granularity (model/batch/serial) and compound identifier support. @en
  • eqp - Aircraft Equipment Vocabulary
    https://data.nasa.gov/ontologies/atmonto/equipment#
    Defines aircraft models, aircraft systems / subsystems, and aircraft characteristics @en
  • wty - Verisav Warranty & Contracts Vocabulary
    https://ns.verisav.fr/wty#
    Vocabulary for machine-readable warranties and contracts, enabling complete automation of payment decisions and coverage assessment in after-sales service. @en
  • psn - Product Supply Network Vocabulary
    https://purl.org/psn/vocab#
    A small ontology to model supply networks (supply chains) from all industries through products that are interlinked based on derivational dependencies. @en
  • ce - CityExplorer Ontology
    https://purl.org/cityexplorer
    This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en
  • saref - SAREF: the Smart Appliances REFerence ontology
    https://saref.etsi.org/core/
    The Smart Appliances REFerence (SAREF) ontology is a shared model of consensus that facilitates the matching of existing assets (standards/protocols/datamodels/etc.) in the smart appliances domain. The SAREF ontology provides building blocks that allow separation and recombination of different parts of the ontology depending on specific needs. @en
  • s4inma - SAREF4INMA: an extension of SAREF for the industry and manufacturing domain
    https://saref.etsi.org/saref4inma/
    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
  • cem - Crime Event Model (CEM)
    https://w3id.org/CEMontology
    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
  • hht - Historical Hierarchical Territories
    https://w3id.org/HHT
    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
  • IIoT - Ontology for Industrial Internet of Things systems
    https://w3id.org/IIoT
    The Internet of Things taxonomy is extended with semantic ontologies for IoT layers, containing classes, properties, individuals, and rules specific to IoT technologies, tools, and applications @en
  • gsn - OntoGSN
    https://w3id.org/OntoGSN/ontology
    OntoGSN is an ontology for managing assurance cases in the Goal Structuring Notation (GSN). The goal of the ontology is to help users in linking the elements of their cases - claims and evidence - with the internationalized resource identifiers (IRIs) of represented concepts, events and data, and in evaluating the validity of their argument. @en
  • cevent - Cultural Event Ontology (ArCo network)
    https://w3id.org/arco/ontology/cultural-event
    The Cultural Event module models cultural events, i.e. events involving cultural properties. @en
  • cdc - CDC: Construction Dataset Context ontology
    https://w3id.org/cdc
    The Construction Dataset Context (CDC) ontology is an extension of DCAT v2.0, a W3C Recommendation ontology for describing (RDF and non-RDF) datasets published on the Web. Using this extension, it becomes possible to describe a context for construction-related datasets that are being distributed using Web technology as well as datasets that are not shared outside an organization such as local copies, work in progress and other datasets that remain internal. This dataset metadata encompasses the temporal context (period or snapshot), the type of content of the dataset (as-built, design, etc.) and relations between contextualized datasets (previous as-built, requirements related to a design, etc.). In addition, this DCAT extension also provides terminology for managing dataset distributions that are scoped to a certain (named or default) graph of an RDF file or quadstore. @en