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The nanopub ontology @en
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The Ontology of units of Measure (OM) 2.0 models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. @en
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The provenance ontology supports data management and auditing tasks. It is used to define the different types of named graphs we used in the store (quad store) and enables their association with metadata that allow us to manage, validate and expose data to BBC services @en
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The Data Knowledge Vocabulary allows for a comprehensive description of data assets and enterprise data management. It covers a business data dictionary, data quality management, data governance, the technical infrastructure and many other aspects of enterprise data management. The vocabulary represents a linked data implementation of the Data Knowledge Model which resulted from extensive applied research. @en
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The Open NEE Configuration Model defines a Linked Data-based model for describing a configuration supported by a Named Entity Extraction (NEE) service. It is based on the model proposed in "Configuring Named Entity Extraction through Real-Time Exploitation of Linked Data" (http://dl.acm.org/citation.cfm?doid=2611040.2611085) for configuring such services, and allows a NEE service to describe and publish as Linked Data its entity mining capabilities, but also to be dynamically configured. @en
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CiteDCAT-AP is an extension of the DCAT application profile for data portals in Europe (DCAT-AP) for describing resources documented by using the DataCite metadata schema - the de facto standard for data citation, and used across scientific disciplines. Its basic use case is to make research data searchable on general data portals, thereby bridging the gap between scientific and public sector information. For this purpose, CiteDCAT-AP provides an RDF vocabulary and the corresponding RDF syntax binding for the metadata elements defined in DataCite. @en
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This document is a vocabulary to describe compound measures, i.e. measures with several metric or item that are organized with serveral dimensions. The description of such a measure relies on a Tree-Structure of Requirement (TSoR): a set of requirements structured hierarchicaly with analysis element. A TSoR represents the main measure. Several information may be added to explicitely indicate how the overall score on the measure should be calculated based on the hierarchy, relative importance of the node of the hierarchy and an aggregation function. The measure can be described completely and unambiguously from the organisation to the requirements and the implementation. @en
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domOS Common Ontology (dCO) represents a common information model to share a unified understanding for humans and machines and to ensure semantic interoperability in a heterogeneous IoT infrastructure. This ontology allows the decoupling of the infrastructure from the software services and applications. @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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This ontology is an evolution of IRE ontology. It describes identification of resources on the Web, through the definition of relationships between resources and their representations on the Web. The requirement is to describe what can be identified by URIs and how this is handled e.g. in form of HTTP requests and reponds. @en
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OPMW is a OPMV profile to model the executions and definitions of scientific workflows. @en
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SAREF4INMA is an extension of SAREF for the industry and manufacturing domain. SAREF4INMA focuses on extending SAREF for the industry and manufacturing domain to solve the lack of interoperability between various types of production equipment that produce items in a factory and, once outside the factory, between different organizations in the value chain to uniquely track back the produced items to the corresponding production equipment, batches, material and precise time in which they were manufactured. SAREF4INMA is specified and published by ETSI in the TS 103 410-5 associated to this ontology file. SAREF4INMA was created to be aligned with related initiatives in the smart industry and manufacturing domain in terms of modelling and standardization, such as the Reference Architecture Model for Industry 4.0 (RAMI), which combines several standards used by the various national initiatives in Europe that support digitalization in manufacturing. The full list of use cases, standards and requirements that guided the creation of SAREF4INMA are described in the associated ETSI TR 103 507. @en
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IoT-O is a core domain Internet of Things ontology. It is intended to model horizontal knowledge about IoT systems and applications, and to be extended with vertical, application specific knowledge. It is constituted of different modules : - A sensing module, based on W3C's SSN (http://purl.oclc.org/NET/ssnx/ssn) - An acting module, based on SAN (http://www.irit.fr/recherches/MELODI/ontologies/SAN) - A service module, based on MSM (http://iserve.kmi.open.ac.uk/ns/msm/msm-2014-09-03.rdf) and hRest (http://www.wsmo.org/ns/hrests) - A lifecycle module, based on a lifecycle vocabulary (http://vocab.org/lifecycle/schema-20080603.rdf) and an iot-specific extension (http://www.irit.fr/recherches/MELODI/ontologies/IoT-Lifecycle) - An energy module, based on powerOnt (ttp://elite.polito.it/ontologies/poweront.owl) IoT-O developping team also contributes to the oneM2M IoT interoperability standard. @en
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The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any 'thing', whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. @en
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An ontology defining weather-related concepts and properties being relevant to smart home systems that provide predictive control. @en