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The File Ontology for Geometry formats (FOG) describes meaningful relations towards geometry snippets in RDF literals, geometry files on relative or absolute URLs and ontology-based geometry descriptions. The defined properties in this ontology are related towards each other and additional metadata is provided, such as file extension and related specifications/sources (incl. entries in dbpedia and Wikidata). The initial version of the ontology (v0.0.1) was documented in: Bonduel, Mathias, Wagner, Anna, Pauwels, Pieter, Vergauwen, Maarten, & Klein, Ralf (2019). Including Widespread Geometry Formats in Semantic Graphs Using RDF Literals. In Proceedings of the European Conference on Computing in Construction (EC3 2019). Chania, Greece. @en
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The Geometry Metadata Ontology contains terminology to Coordinate Systems (CS), length units and other metadata (file size, software of origin, etc.). GOM is designed to be at least compatible with OMG (Ontology for Managing Geometry) and FOG (File Ontology for Geometry formats), and their related graph patterns. In addition, GOM provides terminology for some experimental data structures to manage (marked as vs:term_status = unstable): * transformed geometry (e.g. a prototype door geometry that is reused for all doors of this type). This is closely related to the transformation of Coordinate Systems @en
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The Ontology for Managing Geometry (OMG) is an ontology for describing geometry descriptions of objects. It provides means to support the application of multiple geometry descriptions of the same object as well as the description of the geometry evolution over time. The OMG is based the concepts introduced in the Ontology for Property Management (OPM) ontology. This ontology was created within the research project SCOPE, funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The initial version of the ontology (v0.0.1) is documented in: Wagner, Anna, Bonduel, Mathias, Pauwels, Pieter & Rüppel, Uwe(2019). Relating Geometry Descroptions to its Derivatives on the Web. In Proceedings of the European Conference on Computing in Construction (EC3 2019). Chania, Greece. @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