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The DNS Security Ontology (DSecO) project is a data model for representing and reasoning on Domain Name System (DNS) data. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing a DNS Knowledge Graph (KG) for administration and security assessment applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g. with `rdfs:subClassOf`, `owl:equivalentClass`). Directions for direct instanciation of these vocabularies are provided for cases where implementing a class/property alignment is redundant. Alignment holds for the following vocabulary releases: - [ORG](https://www.w3.org/TR/vocab-org/) 0.8 - [UCO](https://github.com/ucoProject/uco) Release-0.8.0 @en
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The scope of the DIO is the domain of design intent or design rationale that needs to be documented while undertaking the design of any artifact @en
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The DINGO ontology (Data Integration for Grant Ontology) defines the terms of the DINGO vocabulary and provides a machine readable extensible framework to model data relative to projects, funding, project and funding actors, and, notably, funding policies. It is designed to yield high modeling power and elasticity to cope with the huge variety in funding and project practices, which makes it applicable to many areas where funding is an important aspect: first of all research, but also the arts, cultural conservation, and many others. @en
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This ontology defines a vocabulary for describing carbon emission conversion factors (CF). These are values typically used to calculate carbon emissions where the CF multiplies a quantified estimate of the energy (e.g., kWh of electricity, litters of fuel, etc.) used by a particular activity. @en
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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 I-ADOPT Framework ontology @en
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CTRLont specifies concepts and relationships of control actors on a high level @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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This ontology defines classes and properties for describing participants, infrastructure, data and services of the International Data Spaces (formerly known as Industrial Data Space). @en
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OWL ontology for the IFC conceptual data schema and exchange file format for Building Information Model (BIM) data @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 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