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  • 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
  • pghdprovo - Patient Generated Health Data Provenance Ontology
    https://w3id.org/pghdprovo
    Patient Generated Health Data (PGHD) refer to health data collected by patient or their relatives. This ontology seeks to capture information about the data, the provenance and data quality associated with PGHD shared with an EHR. @en
  • seast - The SEAS Time Ontology.
    https://w3id.org/seas/TimeOntology
    This ontology defines: - a set of subclasses of `seas:Evaluation` to better interpret evaluations of quantifiable properties. - a set of sub properties of `seas:hasProperty` to qualify time-related properties. @en
  • rbdoc - RiverBench documentation ontology
    https://w3id.org/riverbench/schema/documentation
    Ontology with metadata needed to generate documentation of datasets, distributions, profiles, etc. in RiverBench @en
  • rb - RiverBench metadata ontology
    https://w3id.org/riverbench/schema/metadata
    Ontology for describing datasets and profiles in the RiverBench benchmark suite. @en
  • semts - The Semantic Time Series Ontology
    https://w3id.org/semts/ontology#
    SemTS is an ontology designed to identify and describe segments within time series data, which are specific data points or intervals that can overlap. These segments encompass characteristic knowledge about the time interval they cover, including common time series features, structural anomalies, motifs, or information provided by domain experts. By classifying and semantically representing this knowledge, SemTS promotes organized reusability and efficient propagation, potentially reducing resource expenditure while enhancing future analyses. It employs established semantic approaches. Examples are DCAT to reference associated time series data, OWL-Time to define the index structure of time series data and segments or ML-Schema to expand the expressiveness regarding data analysis task information. SemTS's design involves categorizing time series knowledge and mapping it to specific intervals and dimensions of time series data. It introduces a class called TimeSeriesSegment to model these segments, extending the DCAT Dataset class to enable segments to be part of other segments. This structure allows for the association of knowledge, such as anomalies, with particular intervals or data points. TimeIndex specifications extend OWL-Time classes, while dimensional details are represented by DataDimension. The segment-wise consideration of knowledge indirectly serves as an index structure, linking meaningful time series data with categorized knowledge. At the highest level of abstraction, time series knowledge is divided into three categories: DataKnowledge, ScenarioKnowledge, and MethodKnowledge. DataKnowledge refers to insights extracted directly from the data or through analytical methods, such as class membership from time series clustering. ScenarioKnowledge describes verified contexts, including data annotations or domain-specific process knowledge, often equating to expert-provided a priori information and can also define facts derived from inferred knowledge. MethodKnowledge encompasses effective analytical method presets or mathematical/logical equivalents of established process information. @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
  • olca - Ontology Loose Coupling Annotation
    https://w3id.org/vocab/olca
    A vocabulary defining annotations enabling loose coupling between classes and properties in ontologies. Those annotations define with some accuracy the expected use of properties, in particular across vocabularies, without the formal constraints entailed by the use of OWL or RDFS constructions @en
  • gtm - Gouda Time Machine Ontology
    https://www.goudatijdmachine.nl/def
    The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @en
  • gleif-geo - Global Legal Entity Identifier Foundation Geocoding Ontology
    https://www.gleif.org/ontology/Geocoding/
    Ontology defining concepts for Geocoding of addresses. It is based on the geocoding used in the Global Legal Entity Identifier Foundation (GLEIF) Golden Copy Data, but is more broadly applicable. @en
  • sdm - SPARQL endpoint metadata
    https://w3id.org/vocab/sdm
    A small vocabulary for representing SPARQL endpoint metadata on the web @en
  • gleif-base - Global Legal Entity Identifier Foundation Base Ontology
    https://www.gleif.org/ontology/Base/
    Ontology defining generic concepts for reuse by other Global Legal Entity Identifier Foundation (GLEIF) ontologies. It defines generic classes for (legal) Entities and their relationships and statuses; and generic properties for different types of name and address. It makes use of the OMG Languages Countries and Codes (LCC) ontology (based on the ISO 3166 standard) for country and region information. @en