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The SEAS Device ontology defines `seas:Device` as physical system that are designed to execute one or more procedures that involve the physical world. @en
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The SEAS Forecasting ontology extends the [Procedure Execution ontology (PEP)](https://w3id.org/pep/) @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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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 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
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Smart home ontology for weather phenomena and exterior conditions @en
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The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @en
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Module for data schema specifications, part of the W3C Web of Things (WoT) Thing Description model @en
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Security mechanisms for the the Web of Thing @en
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This ontology aims to model the Web of Things domain according to the W3C Interest Group (http://w3c.github.io/wot/) @en
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This ontology describes sensors and observations, and related concepts. It does not describe domain concepts, time, locations, etc. as these are intended to be included from other ontologies via OWL imports. @en