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An entry sub-ontology of time (OWL-Time). @en
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The ISA Programme Location Core Vocabulary provides a minimum set of classes and properties for describing any place in terms of its name, address or geometry. The vocabulary is specifically designed to aid the publication of data that is interoperable with EU INSPIRE Directive. @en
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The Ontology for Media Resources 1.0 describes a core vocabulary of properties and a set of mappings between different metadata formats of media resources hat describe media resources published on the Web (as opposed to local archives, museums, or other non-web related and non-shared collections of media resources). @en
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Defines temporal / spatial concepts and general-purpose datastructures @en
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The notion of territory plays a major role in human and social sciences. In an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories,we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry @en
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The module Location models information related to the localization and georeferencing of a cultural property. In this module are used as template the following Ontology Design Patterns: - http://www.ontologydesignpatterns.org/cp/owl/collectionentity.owl - http://www.ontologydesignpatterns.org/cp/owl/classification.owl - http://www.ontologydesignpatterns.org/cp/owl/place.owl - http://www.ontologydesignpatterns.org/cp/owl/timeindexedsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
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Ontology that defines the topology of damages in constructions. @en
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An RDF vocabulary to describe and facilitate the usage of a Multidimensional Interface. @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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The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @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 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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RiC-O (Records in Contexts-Ontology) is an OWL ontology for describing archival record resources. As the second part of Records in Contexts standard, it is a formal representation of Records in Contexts Conceptual Model (RiC-CM). The current official version is <html:strong>v0.2</html:strong>; it is compliant with RiC-CM v0.2 full draft, that will be published in February or March 2021, and that is slightly different from <html:a href="https://www.ica.org/sites/default/files/ric-cm-0.2_preview.pdf">RiC-CM v0.2 preview, that was published in December 2019. RiC-O provides a generic vocabulary and formal rules for creating RDF datasets (or generating them from existing archival metadata) that describe in a consistent way any kind of archival record resource. It can support publishing RDF datasets as Linked Data, querying them using SPARQL, and making inferences using the logic of the ontology. @en
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Ontology designed to provide an RDF representation of Hypermedia Controls, in particular links and forms. @en