162
results
  • ontolex - Lexicon Model for Ontologies - Core
    http://www.w3.org/ns/lemon/ontolex
    A model for the representation of lexical information relative to ontologies. Core module. @en
  • synsem - Lexicon Model for Ontologies - Synsem
    http://www.w3.org/ns/lemon/synsem
    A model for the representation of lexical information relative to ontologies. Syntax and semantics module. @en
  • ssn - Semantic Sensor Network Ontology
    http://www.w3.org/ns/ssn/
    This ontology describes sensors, actuators and observations, and related concepts. It does not describe domain concepts, time, locations, etc. these are intended to be included from other ontologies via OWL imports. @en
  • uiot - Urban IoT Ontologies - Core Module
    http://www.w3id.org/urban-iot/core
    Core module of the suite of ontologies for urban IoT devices. @en
  • uiote - Urban IoT Ontologies - Electric Mobility Module
    http://www.w3id.org/urban-iot/electric
    Electric Mobility module of the suite of ontologies for urban IoT devices. @en
  • uiots - Urban IoT Ontologies - Sharing Mobility Module
    http://www.w3id.org/urban-iot/sharing
    Sharing Mobility module of the suite of ontologies for urban IoT devices. @en
  • ui - A user interface ontology
    http://www.w3.org/ns/ui
    An ontology suitable for describing forms, sequences in widgets @en
  • caso - Context Aware System Observation Ontology
    http://www.w3id.org/def/caso#
    CASO (Context Aware System Observation) is an ontology for context aware system and observation services. Its goal is to describe all the processing of the context. @en
  • sosa - Sensor, Observation, Sample, and Actuator (SOSA) Ontology
    http://www.w3.org/ns/sosa/
    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
  • solid - Solid terms
    http://www.w3.org/ns/solid/terms
    Solid terms @en
  • atts - Air Traffic Temporal and Spacial Vocabulary
    https://data.nasa.gov/ontologies/atmonto/general#
    Defines temporal / spatial concepts and general-purpose datastructures @en
  • decision - Decision ontology
    https://decision-ontology.googlecode.com/svn/trunk/decision.owl
    Decision-making is a process that can result in some decision and decision is a situation of indicating one of the considered options. Decision Ontology provides means for precise distinguishing and distinct treatment of these two aspects. @en
  • tresiot - Ontology for Trust Recommendation in Social Internet of Things
    https://liidr.org/trust-recommendation-in-social-internet-of-things/
    This ontology models trust recommendation concepts in SIoT to bridge the gap between abstract trust concepts and real-world device concepts. @en
  • atd - Air Traffic Data
    https://data.nasa.gov/ontologies/atmonto/data#
    Defines concepts related to airport status, including weather, forecasts, and airport operations @en
  • vas - VAS. A Semantic Model for Earth Observation Remote Sensing
    https://robotica.uv.es/proyectos/ASOTVAS/def/ciencia-tecnologia/vas
    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