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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
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This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @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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A reference implementation of the OntoUML metamodel in OWL. @en
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The Ontology for Property Management (OPM) extends the concepts introduced in the Smart Energy Aware Systems (SEAS) Evaluations ontology. @en
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This ontology describes the components, failures, sensors, and events related to offshore wind platforms. @en
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Used for indexing, searching and comparing Open Source Hardware projects @en
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This ontology defines a vocabulary for describing provenance traces of carbon emission calculations by capturing the quantifiable measurements of carbon emission sources used by some activities (e.g., electricity used by a machinery to produce a product, petrol used to make a car journey, etc.) and emission conversion factors used to estimate the carbon emissions produced by these. In addition, the ontology provides the ability to capture various data transformations that occurred before energy estimates may be used with relevant conversion factors. For example, sensors may provide raw readings about a water flow of an irrigation rig in an agri-food operation which is then used as a proxy to estimate the total volume of fertilisers used. @en
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The process execution ontology is a proposal for a simple extension of both the [W3C Semantic Sensor Network](https://www.w3.org/TR/vocab-ssn/) and the [Semantic Actuator Network](https://www.irit.fr/recherches/MELODI/ontologies/SAN.owl) ontology cores. @en
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The Procedural Knowledge Ontology (PKO) addresses the Procedural Knowledge (PK) domain, and models procedures, their executions, and related resources and agents. @en
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An ontology to model accountability of AI systems which use machine learning. @en
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The REACT ontology aims to represent all the necessary knowledge to support the achievement of island energy independence through renewable energy generation and storage, a demand response platform, and promoting user engagement in a local energy community. The REACT ontology has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @en
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This ontology defines batteries and their state of charge ratio property. @en
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The SEAS Building ontology describes a taxonomy of buildings, building spaces, and rooms. Some categorizations are based on the energy efficiency related to their insulation etc., although the actual values for classes depend the country specific regulations and geographical locations. Other categorizations are based on occupancy and activities. There is no single accepted categorization available. This taxonomy uses some types selected from: - International building occupancy based categories (USA) - The Classification of Types of Constructions (EU) - Finnish building categorization VTJ2000 (Finland) - Wikipedia category page for Rooms: https://en.wikipedia.org/wiki/Category:Rooms @en