A general purpose ontology for observable properties. The ontology supports description of both qualitative and quantitative properties. The allowed scale or units of measure may be specified. A property may be linked to substances-or-taxa and to features or realms, if they play a role in the definition. @en
This vocabulary set can represent 5W1H (Who, What, When, Where, Why, How) in event (scene) descriptions. This file is also provided in Knowledge Graph Reasoning Challenge. @en
This is a registration of classes and properties from International Standard Bibliographic Description (ISBD), consolidated edition, published by De Gruyter Saur in July 2011 (ISBN 978-3-11-026379-4). @en
The Linked SPARQL Queries Vocabulary (LSQ(V)), defined using RDF(S) and OWL, provides a machine readable vocabulary to help describe queries in SPARQL logs and their statistics. The vocabulary builds upon the SPIN vocabulary and the Service Description vocabulary. @en
TEACH, the Teaching Core Vocabulary, is a lightweight vocabulary providing terms to enable teachers to relate things in their courses together. The Teaching Core Vocabulary is based on practical requirements set by providing seminar and course descriptions as Linked Data. @en
LSC, the Linked Science Core Vocabulary, is a lightweight vocabulary providing terms to enable publishers and researchers to relate things in science to time, space, and themes. @en
MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-core layer represents the core information gathered from a basic machine learning experiment design. @en
MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-algo layer represents the algorithm information existing into a basic machine learning experiment. @en
MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-perf layer is the 3rd level of the MEX for representing the machine learning algorithm's performances. @en
The EduProgression ontology formalizes the educational progressions of the French educational system, making possible to represent the existing progressions in a standard formal model, searchable and understandable by machines (OWL). @en