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This is an RDF/OWL representation of the GeoSciML Geologic Timescale model, which has been adapted from the model described in Cox, S.J.D, & Richard, S.M. (2005) A formal model for the geologic timescale and GSSP, compatible with geospatial information transfer standards, Geosphere, Geological Society of America 1/3, 119–137. @en
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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 for describing biographical information about people, both living and dead. @en
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A vocabulary for describing relationships between people @en
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WAI vocabulary aims to extend the FOAF specification through introducing the concepts of roles and profiles. In society, people are more than just persons, they can be musicians, presidents of government, firemen, football players or car drivers in a traffic jam. @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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The CWRC Ontology is the ontology of the Canadian Writing Research Collaboratory. @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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Ontology for legal entity identifier registration. It was designed for Global Legal Entity Identifier Foundation (GLEIF) Level 1 data corresponding to the Common Data Format version 2.1. It covers key reference data for a legal entity identifiable with an LEI. The ISO 17442 standard developed by the International Organization for Standardization defines a set of attributes or LEI reference data that comprises the most essential elements of identification. It specifies the minimum reference data, which must be supplied for each LEI: The official name of the legal entity as recorded in the official registers. The registered address of that legal entity. The country of formation. The codes for the representation of names of countries and their subdivisions. The date of the first LEI assignment; the date of last update of the LEI information; and the date of expiry, if applicable. @en
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Ontology for legal entity parent relationships. It was designed for Global Legal Entity Identifier Foundation (GLEIF) Level 2 data corresponding to the Relationship Record format, version 1.1. Legal entities that have or acquire an LEI report their ‘direct accounting consolidating parent’ as well as their ‘ultimate accounting consolidating parent’, or for International Branches ‘is an International Branch of'. Otherwise they must provide a Reporting Exception. @en
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The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @en