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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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An ontology for describing software and their links to inputs, outputs and variables. The ontology extends schema.org and codemeta vocabularies @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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Specification of the metadata used to describe models in the OntoUML/UFO Catalog. @en
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Ontology with metadata needed to generate documentation of datasets, distributions, profiles, etc. in RiverBench @en
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Ontology for describing datasets and profiles in the RiverBench benchmark suite. @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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An ontology to describe a Semantic-Web Machine Learning System (SWeMLS) @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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A vocabulary defining annotations enabling loose coupling between classes and properties in ontologies. Those annotations define with some accuracy the expected use of properties, in particular across vocabularies, without the formal constraints entailed by the use of OWL or RDFS constructions @en