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  • The Vocabulary for Ranking (vRank) is an RDF Schema vocabulary for materializing ranking computations. @en
  • The Data Template (DT) Ontology is based on concepts and principles for creating templates from ISO 23387 and the associated XML data schema, which is currently under development. @en
  • The Internet of Construction Ontology (IoC) construction process ontology is intended to represent a comprehensive solution of how processes in the construction industry can be modelled. Due to the iterative nature of creating an ontology, the construction process ontology presented here can at best be considered a working state at the time of publication. Our approach emphasizes the simplest and most comprehensive mapping possible, which is only extended based on insights from practical use when otherwise compelling limitations in usability and applicability arise. Thus, the extension and refinement of the developed construction process ontology strongly depends on the integration of further areas of the construction value chain and the connection of further domain ontologies. @en
  • This ontology aims to model RDF streams, their metadata, and access endpoints for publishing and consuming these streams @en
  • The Counter Ontology specification provides basic concepts and properties for describing a general counter concept and some important sub counters @en
  • The Weighting Ontology specification provides a vocabulary for describing weightings and their referenced scales, on/ for the Semantic Web. @en
  • The RECO ontology defines the vocabulary for representing preferences-as-constraints and preferences-as-ratings as RDF graphs. This lightweight vocabulary provides domain-independent means to describe user profiles in a coherent and context-aware way. RECO has been designed as an extension of both Friend-Of-A-Friend (FOAF) and Who Am I! (WAI) ontologies. @en
  • This is an ontology representation of the climatic data variables 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
  • This ontology is part of the Agriculture Meteorology example showcasing the ontology developed by the W3C Semantic Sensor Networks incubator group (SSN-XG). It is published here in order to generalize the potential usage and the alignment with other standardization efforts of the SSN ontology. @en
  • This ontology is partially based on the SysML QUDV (Quantities, Units, Dimensions and Values) proposed by a working group of the SysML 1.2 Revision Task Force (RTF), working in close coordination with the OMG MARTE specification group. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, the QU ontology has been further developed as a complement to the Agriculture Meteorology example showcasing the ontology developed by the W3C Semantic Sensor Networks incubator group (SSN-XG). @en
  • SemSur, the Semantic Survey Ontology, is a core ontology for describing individual research problems, approaches, implementations and evaluations in a structured, comparable way. @en
  • The FIESTA-IoT ontology takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 ontology), WGS84, IoT-lite, Time, and DUL ontology. @en
  • The IoT-Taxonomy-lite is adapted from M3-lite taxonomy. This taxonomy is refactored and defines many other concepts such as subclasses of Feature-of-Interest and Quality-of-Observation. @en
  • M3 lite taxonomy is designed for the FIESTA-IOT H2020 EU project. We refactor, clean and simplify the M3 ontology designed by Eurecom (Amelie Gyrard). M3 ontology lite is currently aligned with the quantity taxonomy used by several testbeds: SmartSantander (Spain), University of Surrey (United Kingdom), KETI (Korea) and Com4Innov (France). @en