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The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. @en
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The Denotative Description module encodes the characteristics of a cultural property, as detectable and/or detected during the cataloguing process and measurable according to a reference system. Examples include measurements e.g. length, constituting materials e.g. clay, employed techniques e.g. melting, conservation status e.g. good, decent, bad. 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/descriptionandsituation.owl - http://www.ontologydesignpatterns.org/cp/owl/situation.owl @en
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The Construction Dataset Context (CDC) ontology is an extension of DCAT v2.0, a W3C Recommendation ontology for describing (RDF and non-RDF) datasets published on the Web. Using this extension, it becomes possible to describe a context for construction-related datasets that are being distributed using Web technology as well as datasets that are not shared outside an organization such as local copies, work in progress and other datasets that remain internal. This dataset metadata encompasses the temporal context (period or snapshot), the type of content of the dataset (as-built, design, etc.) and relations between contextualized datasets (previous as-built, requirements related to a design, etc.). In addition, this DCAT extension also provides terminology for managing dataset distributions that are scoped to a certain (named or default) graph of an RDF file or quadstore. @en
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The PROTON Top module represents the most general classes @en
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The SEAS Operating Ontology defines evaluations of operating features of interest. @en
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ERA ontology for verified permissions, as applied in vehicle(type) authorisations, registrations and approvals @en
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This specification describes National Library of Korea Ontology vocaburaries using W3C's RDF and OWL technology. @en
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This ontology defines generic concepts related to the life cycle of resource or service. @en
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Used to describe a location that consists of a number of Regions but where the order is not known. e.g. the oddly named order() keyword in a INSDC file. @en
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The goal of LAWD is to fill in the cracks between the data used and published by projects with a focus on the ancient world and the standard Linked Data vocablary schemes, like Dublin Core, the Open Annotation Collaboration, and CIDOC-CRM. @en
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SCoRO, the Scholarly Contributions and Roles Ontology, is an ontology for use by authors and publishers for describing the contributions that may be made and the roles that may be held by a person with respect to a journal article or other publication, and by research administrators and others for describing contributions and roles with respect to other aspects of scholarly research. @en
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The Internet of Construction Ontology (IoC) construction process ontology is intended to represent a comprehensive solution of how processes in the construction industry can be modelled. Due to the iterative nature of creating an ontology, the construction process ontology presented here can at best be considered a working state at the time of publication. Our approach emphasizes the simplest and most comprehensive mapping possible, which is only extended based on insights from practical use when otherwise compelling limitations in usability and applicability arise. Thus, the extension and refinement of the developed construction process ontology strongly depends on the integration of further areas of the construction value chain and the connection of further domain ontologies. @en
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The Databugger ontology describes concepts used in Databugger, a test driven data-debugging framework that can run automatically generated (based on a schema) and manually generated test cases against an endpoint. @en
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The Context Description module includes models for the context of a cultural property, in a broad sense: agents (e.g.: author, collector, copyright holder), objects (e.g.: inventories, bibliography, protective measures, other cultural properties, collections etc.), activities (e.g.: surveys, conservation interventions), situations (e.g.: commission, coin issuance, estimate, legal situation) related, involved or involving the cultural property. Thus it represents attributes that do not result from a measurement of features in a cultural property, but are associated with it. @en
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The BCI ontology specifies a foundational metadata model set for real-world multimodal Brain Computing Interface (BCI) data capture activities. The ontology defines a minimalist and simple abstract metadata foundational model for real-world BCI applications that monitors human activity in any scenario. BCI multimodal domain applications are encouraged to extend and use this ontology in their implementations. @en