Other IT standards

The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given.

SAREF4SYST defines Systems, Connections between systems, and Connection Points at which systems may be connected.

SEAS ontology describes energy systems and their interactions.

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.

SAREF4CITY is an extension of SAREF for the Smart Cities domain. This extension has been created by investigating resources from potential stakeholders of the ontology, such as standardization bodies, associations, IoT platforms and European projects and initiatives. Taking into account ontologies, data models, standards and datasets provided by the identified stakeholders, a set of requirements were identified and grouped in the following categories: Topology, Administrative Area, City Object, Event, Measurement, Key Performance Indicator, and Public Service.

SAREF4BLDG is an extension of the SAREF ontology that was created based on the Industry Foundation Classes (IFC) standard for building information. It should be noted that not the whole standard has been transformed since it exceeds the scope of this extension, which is limited to devices and appliances within the building domain.

ISO/IEC 21000-19:2010 describes MPEG-21 Media Value Chain Ontology (MVCO). The MVCO may be used to capture knowledge about media value chains and to represent it in a computer readable way, concepts in the domain and the relationships between those concepts. ISO/IEC 21000-19:2010 describes the following technology. Model: the model is described in Clause 6, by way of a narrative description of the Value Chain, its main elements and relations. Representation: the MVCO has been formalised as a normative OWL Ontology, and the description of which is given in this Clause. The description consists of listing the classes, the object properties, the datatype properties, and the class individuals. Classes are described by giving the name, an English definition, the class hierarchy, and the restrictions imposed on the class. The representation is given in Clause 7. Annex B contains the normative OWL (XML/RDF) comprising the entire semantics of the elements in the model. Ontology use: an Informative section is provided with non normative descriptions of use, extensions and an API (Annex A).

The Energy Grid ontology
describes power stations, substations, energy resources, and
customers.

This standard extends IEEE 1872-2015 Standard for Ontologies for Robotics and Automation to represent additional domain-specific concepts, definitions, and axioms commonly used in Autonomous Robotics (AuR). This standard is general and can be used in many ways - for example, to specify the domain knowledge needed to unambiguously describe the design patterns of AuR systems, to represent AuR system architectures in a unified way, or as a guideline to build autonomous systems consisting of robots operating in various environments.

ISO 14199:2015 defines a set of models collectively referred to as the Biomedical Research Integrated Domain Group (BRIDG) model for use in supporting development of computer software, databases, metadata repositories, and data interchange standards. It supports technology solutions that enable semantic (meaning-based) interoperability within the biomedical/clinical research arena and between research and the healthcare arena. The clinical research semantics are represented as a set of visual diagrams which describe information relationships, definitions, explanations, and examples used in protocol-driven biomedical research. These diagrams are expressed using the iconography and grammar of the Unified Modelling Language (UML), the HL7 Reference Information Model (RIM), and a Web Ontology Language (OWL). ISO 14199:2015 establishes the links between protocol-driven research and its associated regulatory artefacts including the data, organization, resources, rules, and processes involved in the formal assessment of the utility, impact, or other pharmacological, physiological, or psychological effects of a drug, procedure, process, subject characteristic, or device on a human, animal, or other subject or substance along with all associated regulatory artefacts required for or derived from this effort, including data specifically associated with post-marketing adverse event reporting.

ISO 14199:2015 defines a set of models collectively referred to as the Biomedical Research Integrated Domain Group (BRIDG) model for use in supporting development of computer software, databases, metadata repositories, and data interchange standards. It supports technology solutions that enable semantic (meaning-based) interoperability within the biomedical/clinical research arena and between research and the healthcare arena. The clinical research semantics are represented as a set of visual diagrams which describe information relationships, definitions, explanations, and examples used in protocol-driven biomedical research. These diagrams are expressed using the iconography and grammar of the Unified Modelling Language (UML), the HL7 Reference Information Model (RIM), and a Web Ontology Language (OWL).

CASO (Context Aware System Observation) is an ontology for context aware system and observation services. Its goal is to describe all the processing of the context.