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aml - AutomationML Ontology https://w3id.org/i40/aml A vocabulary to represent the AutomationML Standard - IEC 62714 @en -
iddo - The Interconnected Data Dictionary Ontology (IDDO) https://w3id.org/iddo The interconnected data dictionary ontology maps the data model of the ISO 23386 for the describing, creating, and maintenance of properties in interconnected data dictionaries. @en -
iadopt - I-ADOPT Framework ontology https://w3id.org/iadopt/ont The I-ADOPT Framework ontology @en -
core - Materials Design Ontology - Core Module https://w3id.org/mdo/core/ This is the core module in Materials Design Ontology. @en -
isoprops - ISO 23386 Property Ontology (ISOProps) https://w3id.org/isoprops 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 -
ontouml - OntoUML Metamodel Vocabulary https://w3id.org/ontouml A reference implementation of the OntoUML metamodel in OWL. @en -
ocmv - OntoUML/UFO Catalog Metadata Vocabulary https://w3id.org/ontouml-models/vocabulary Specification of the metadata used to describe models in the OntoUML/UFO Catalog. @en -
nno - The Neural Network Ontology https://w3id.org/nno/ontology This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en -
react - The REACT Ontology https://w3id.org/react 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 -
seas - SEAS ontology https://w3id.org/seas/ This vocabulary is version v0.1 of the ITEA2 Smart Energy Aware Systems project vocabulary. It enables the description of electricity measurements of a site using the Data Cube W3C vocabulary. @en -
seasb - The SEAS Battery ontology. https://w3id.org/seas/BatteryOntology This ontology defines batteries and their state of charge ratio property. @en -
seasbo - The SEAS Building Ontology https://w3id.org/seas/BuildingOntology 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 -
semts - The Semantic Time Series Ontology https://w3id.org/semts/ontology# 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 -
seo - The Scientific Events Ontology https://w3id.org/seo The vocabulary allows for the description of data about scientific events such as conferences, symposiums and workshops. @en -
seas-eval - The SEAS Evaluation ontology https://w3id.org/seas/EvaluationOntology The Evaluation ontology describes evaluation of [`seas:Property`ies](https://w3id.org/seas/Property). There may be: - direct evaluations, or - qualified evaluations. @en