691
results
How to
  • 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
  • seas-op - The SEAS Failable System ontology
    https://w3id.org/seas/OperatingOntology
    The SEAS Operating Ontology defines evaluations of operating features of interest. @en
  • seas-qudt - QUDT Alignment.
    https://w3id.org/seas/QUDTAlignment
    This ontology defines proposed alignemnts with the QUDT ontology. @en
  • sri - Smart Readiness Indicator Vocabulary
    https://w3id.org/sri
    A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en
  • tribont-core - Core module
    https://w3id.org/tribont/core
    The goal of this module is to represent the common classes, and object and data properties included in two or more modules of the TribOnt ontology. @en
  • usability - Usability
    https://w3id.org/usability
    Ontology 'Usability' created to describe and store information about interactions of user with a software user interface @en
  • gleif-ra - Global Legal Entity Identifier Foundation Registration Authority Ontology
    https://www.gleif.org/ontology/RegistrationAuthority/
    Ontology defining concepts for Business Registries, including the jurisdictions served. This is based on the Registration Authority Code List (RAL) used for Global Legal Entity Identifier Foundation (GLEIF) registration, but 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
  • olca - Ontology Loose Coupling Annotation
    https://w3id.org/vocab/olca
    A vocabulary defining annotations enabling loose coupling between classes and properties in ontologies. Those annotations define with some accuracy the expected use of properties, in particular across vocabularies, without the formal constraints entailed by the use of OWL or RDFS constructions @en
  • sdm - SPARQL endpoint metadata
    https://w3id.org/vocab/sdm
    A small vocabulary for representing SPARQL endpoint metadata on the web @en
  • jsonsc - JSON Schema in RDF
    https://www.w3.org/2019/wot/json-schema#
    Module for data schema specifications, part of the W3C Web of Things (WoT) Thing Description model @en