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  • 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
  • todo - TODO: Task-Oriented Dialogue management Ontology
    https://w3id.org/todo
    With the aim of enhancing natural communication between workers in industrial environments and the systems to be used by them, TODO (Task-Oriented Dialogue management Ontology) has been developed to be the core of task-oriented dialogue systems. TODO is a core ontology that provides task-oriented dialogue systems with the necessary means to be capable of naturally interacting with workers (both at understanding and at ommunication levels) and that can be easily adapted to different industrial scenarios, reducing adaptation time and costs. Moreover, it allows to store and reproduce the dialogue process to be able to learn from new interactions. @en
  • tddfa - TODODFA: Frame-Action Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododfa
    Module for Action (and Frame) modelling inside domain. @en
  • tddial - TODODial: Dialogue Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododial
    Module for dialogue process and system output management. @en
  • tddm - TODODM: Dialogue Management Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododm
    Module for dialogue process and system output management. @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
  • seasto - The SEAS Trading ontology
    https://w3id.org/seas/TradingOntology
    The Seas Trading Ontology defines concepts and relations to describe ownership, trading, bilateral contracts and market licenses: - players own systems and trade commodities, which have a price; - bilateral electricity contracts are connections between electricity traders at which they exchange electricity; - electricity markets are connections between electricity traders at which they exchange electricity, using a market license; - electricity markets can be cleared, and balanced; - evaluations can have a traded volume validity context @en
  • sw-quality - SQuAP Ontology
    https://w3id.org/squap/
    Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been poorly studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. SQaAP-Ont is an OWL ontology that formalises those relations in order to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process characteristics. @en
  • tddw - TODODW: World Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododw
    Module for scenario modelling (world elements) of domain. @en
  • tddt - TODODT: Dialogue Tracing Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododt
    Module for dialogue tracing. @en
  • tddom - TODODom: Domain Module for Task-Oriented Dialogue management Ontology (TODO)
    https://w3id.org/todo/tododom
    Module for domain modelling. @en
  • gtm - Gouda Time Machine Ontology
    https://www.goudatijdmachine.nl/def
    The Gouda Time Machine Ontology describes the geo-temporal classes and properties used within the Gouda Time Machine. @en
  • gleif-elf - Entity Legal Form Ontology
    https://www.gleif.org/ontology/EntityLegalForm/
    Ontology defining concepts for Entity Legal Forms and their abbreviations by jurisdiction, based on ISO 20275. Though used by Global Legal Entity Identifier Foundation (GLEIF) for Legal Entity Identifier registration, it is more broadly applicable. @en
  • gleif-geo - Global Legal Entity Identifier Foundation Geocoding Ontology
    https://www.gleif.org/ontology/Geocoding/
    Ontology defining concepts for Geocoding of addresses. It is based on the geocoding used in the Global Legal Entity Identifier Foundation (GLEIF) Golden Copy Data, but is more broadly applicable. @en