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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 -
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 -
usability - Usability https://w3id.org/usability Ontology 'Usability' created to describe and store information about interactions of user with a software user interface @en -
tree - TREE https://w3id.org/tree A hypermedia specification for fragmenting collections. @en -
tribont - TribOnt ontology https://w3id.org/tribont The goal is to represent the tribological experiments by relaying on the representation included in the core,, sample and equipment modules. @en -
tribont-material - Material module https://w3id.org/tribont/material The goal of this module is to represent the materials that can be involved in the tribological experiments as part of the tested samples. @en -
tribont-core - Core module https://w3id.org/tribont/core The goal of this module is to represent the common classes, and object and data properties included in two or more modules of the TribOnt ontology. @en