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An OWL representation of the model for Temporal Ordinal Reference Systems defined in GeoSciML v3. @en
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FraPPE is a vocabulary to enable Visual Analytics operations on geo-spatial time varying data. By enabling Visual Analytics instruments FraPPE ease the capture, correlation and comparison operations on geo-spatial data from different sources evolving over time @en
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A vocabulary to describe opening hours using calendars (recommended: iCal, RDFCal or schema.org) published on the Web. @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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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
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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
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The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @en