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  • sdom - The Software Description Ontology for Models
    https://w3id.org/okn/o/sdm
    The Software Description Ontology for Models (SDM) expands the software description ontology (SD) to add information about scientific software models. Examples of scientific software models are hydrology models, agriculture models or economy models. @en
  • sdont - The Software Description Ontology
    https://w3id.org/okn/o/sd
    An ontology for describing software and their links to inputs, outputs and variables. The ontology extends schema.org and codemeta vocabularies @en
  • okh - Open Know How (OKH) ontology
    https://w3id.org/oseg/ont/okh
    Used for indexing, searching and comparing Open Source Hardware projects @en
  • nno - The Neural Network Ontology
    https://w3id.org/nno/ontology
    This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en
  • pep - Process Execution ontology.
    https://w3id.org/pep/
    The process execution ontology is a proposal for a simple extension of both the [W3C Semantic Sensor Network](https://www.w3.org/TR/vocab-ssn/) and the [Semantic Actuator Network](https://www.irit.fr/recherches/MELODI/ontologies/SAN.owl) ontology cores. @en
  • seas-stats - The SEAS Statistics ontology.
    https://w3id.org/seas/StatisticsOntology
    This ontology defines common evaluation interpretation concepts for statistics. @en
  • seas-eval - The SEAS Evaluation ontology
    https://w3id.org/seas/EvaluationOntology
    The Evaluation ontology describes evaluation of [`seas:Property`ies](https://w3id.org/seas/Property). There may be: - direct evaluations, or - qualified evaluations. @en
  • foio - The SEAS Feature of Interest ontology.
    https://w3id.org/seas/FeatureOfInterestOntology
    This ontology defines feature of interest and their properties, as an extension of the core classes of the SSN ontology (https://www.w3.org/ns/ssn/). A feature of interest is an abstraction of a real world phenomena (thing, person, event, etc). A feature of interest is then defined in terms of its properties, which are qualifiable, quantifiable, observable or operable qualities of the feature of interest. Alignments to other ontologies are proposed in external documents: - [SSNAlignment](https://w3id.org/seas/SSNAlignment) proposes an alignment to the [SSN ontology](http://www.w3.org/ns/ssn/). - [QUDTAlignment](https://w3id.org/seas/QUDTAlignment) proposes an alignment to the [QUDT ontology](http://qudt.org/). @en
  • saont - The System Accountability Ontology
    https://w3id.org/sao
    An ontology to model accountability of generic systems. @en
  • rains - The RAInS Ontology
    https://w3id.org/rains
    An ontology to model accountability of AI systems which use machine learning. @en
  • pko - Procedural Knowledge Ontology (PKO)
    https://w3id.org/pko
    The Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
  • swemls - Semantic-Web Machine Learning System (SWeMLS) Ontology
    https://w3id.org/semsys/ns/swemls
    An ontology to describe a Semantic-Web Machine Learning System (SWeMLS) @en
  • tido - The Threat Intelligence Decision Ontology
    https://w3id.org/tido#
    The TIDO ontology can be used to describe the decision processes within the threat intelligence domain @en
  • seo - The Scientific Events Ontology
    https://w3id.org/seo
    The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en
  • tribont-sample - Sample module
    https://w3id.org/tribont/sample
    The goal of this module is to represent the sample systems , and the undelaying samples, involved in the tribological experiments. @en