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An ontology to describe experiments, evaluations and their relation. @en
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CHAMEO is a domain ontology designed to model the common aspects across the different characterisation techniques and methodologies. @en
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A simple ontology which implements the Parameter Usage Vocabulary semantic model, as described at https://github.com/nvs-vocabs/P01 @en
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PROV extension for linking Plans and parts of plans to their respective executions. @en
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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 ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. @en
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The Ishikawa ontology aims to provide a data and view model to manage data encoded in Ishikawa diagrams which are also known as fishbone or cause and effect diagram (CED). Ishikawa diagrams result from (iterative) workshops. Thus, the ontology includes the basic modelling of workshops to create Ishikawa diagrams. @en
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The ISO Property (ISOProps) ontology maps the data model of the ISO 23386 for the describing, creating, and maintenance of properties in interconnected data dictionaries. The namespace for ISOProps terms is [https://w3id.org/isoprops](https://w3id.org/isoprops) The preferred prefix for the ISOProps namespace is `isoprops`. ## Ontology Overview  ## Assigning an ISOProps Property to a Feature of Interest  ## Relation between DCAT vocabulary and the ISOProps ontology  @en
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An ontology for metadata about legal texts represented using the LegalHTML format @en
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The Level of Information Need (LOIN) Ontology is defined for specifying information requirements for delivery of data in a buildings' life cycle. The LOIN ontology is based on the standard BS EN 17412-1 (2020). Furthermore, it is extended with vocabulary for connecing Information Delivery Specifications (IDS) and Information containers for linked document delivery (ICDD) as per ISO 21597-1 (2020). @en
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INTRO is an ontology for the fields of literary studies, art studies and intermediality studies for the representation of intertextual, interpictorial, and intermedial relations. It enables the presentation and categorization of diverse features of both textual and pictorial origin and their linking. Its subject area includes the scholarly discourse on these texts/images, interrelations, and features, insofar as research results are also understood as texts with features and relations. @en
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This is the provenance module of Materials Design Ontology. @en
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Metadata for Ontology Description and publication @en
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This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en