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This ontology defines common evaluation interpretation concepts for statistics. @en
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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
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An ontology to describe a Semantic-Web Machine Learning System (SWeMLS) @en
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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
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A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en
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The TIDO ontology can be used to describe the decision processes within the threat intelligence domain @en
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A hypermedia specification for fragmenting collections. @en
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The goal is to represent the tribological experiments by relaying on the representation included in the core,, sample and equipment modules. @en
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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
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The goal of this module is to represent the equipment hierarchy model involved in the tribological experiments. @en
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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
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The goal of this module is to represent the sample systems , and the undelaying samples, involved in the tribological experiments. @en
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VAIR is a taxonomy of AI and risk concepts. @en
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ERA ontology for verified permissions, as applied in vehicle(type) authorisations, registrations and approvals @en
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The Wind Farm Ontology (wfont) describes wind farms and their components. It is inspired by the SANDIA Report SAND2009-1171 and DAEKIN project outcomes. It reuses the AffectedBy and EEP (Execution-Executor-Procedure) ontology design patterns to discover sensors or actuators that observe or act on a given quality or feature of interest. @en