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uiote - Urban IoT Ontologies - Electric Mobility Module http://www.w3id.org/urban-iot/electric Electric Mobility module of the suite of ontologies for urban IoT devices. @en -
sosa - Sensor, Observation, Sample, and Actuator (SOSA) Ontology http://www.w3.org/ns/sosa/ This ontology is based on the SSN Ontology by the W3C Semantic Sensor Networks Incubator Group (SSN-XG), together with considerations from the W3C/OGC Spatial Data on the Web Working Group. @en -
caso - Context Aware System Observation Ontology http://www.w3id.org/def/caso# 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. @en -
atts - Air Traffic Temporal and Spacial Vocabulary https://data.nasa.gov/ontologies/atmonto/general# Defines temporal / spatial concepts and general-purpose datastructures @en -
glc - GLACIATION Metadata Reference Model https://glaciation-project.eu/MetadataReferenceModel The GLACIATION platform develops a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture to reduce energy consumption, improving data processing and optimizing data movement operations. Towards this aim, the platform needs to consume the data and metadata that are fed into the DKG. The metadata can affect and inform the decision-making processes in the GLACIATION architecture and introduces the GLACIATION Metadata Reference Model that will be used for modelling the metadata in the DKG. The GLACIATION Reference Metadata Model makes data ingestion and processing interoperable inside the GLACIATION platform. Linked Data allows for a high level of flexibility and to tackle the variety and merging issues that emerge in heterogenous environments, especially due to the wide range of sensors and other data sources that the platform may integrate. The GLACIATION Reference Metadata Model is tailored to fit the specific purposes of the GLACIATION platform, while the GLACIATION use cases define the scope of the model for better interoperability. There are common metadata challenges for all use cases. This stems from the use of the Kubernetes orchestration system as a basis for the GLACIATION platform. In addition, common to the platform is the ingestion of data from other sources into the DKG that can then be used to affect processing decisions. There are direct data flows from sensors within the system, but also data and metadata from sources external to the system. This allows the system to react e.g. to environmental situations like weather or temperature, but also to requirements concerning security or privacy. Exemplary uses and specializations of the reference model to the GLACIATION use cases are also provided. The GLACIATION Metadata Reference Model can be used for scheduling and performing tasks. The model can be considered as a general conceptualization of a tasks scheduling problem that considers various measuring indicators over the deployed resources. It captures the assignment of time-constrained tasks to time constrained and energy consuming resources, that can satisfy various hard and soft constraints, even compositions of such constraints. The tasks can be monitored through various measuring resources using a variety of single or aggregated, predicted or real measurements. The model is generic, by being both domain and application independent, describing the scheduling tasks, without providing specific solutions on how they can be solved. It can be easily adjusted to each of the current three GLACIATION use cases, covering also the Kubernetes orchestration and its Telemetry System deployed by the project. The proposed model makes it feasible to answer the competency queries defined by each of the Glaciation's use case. @en -
istex - Istex ontology for scholarly documents and extracted entities https://data.istex.fr/ontology/istex# ISTEX is a platform that aims to provide the entire French higher education and research community with an online access to retrospective collections of scientific literature in all disciplines. This unparalleled reservoir of multidisciplinary resources is complemented by a significant number of value-added services that can be used to optimise operations through content discovery and interactive valuation tools. @en -
tresiot - Ontology for Trust Recommendation in Social Internet of Things https://liidr.org/trust-recommendation-in-social-internet-of-things/ This ontology models trust recommendation concepts in SIoT to bridge the gap between abstract trust concepts and real-world device concepts. @en -
atd - Air Traffic Data https://data.nasa.gov/ontologies/atmonto/data# Defines concepts related to airport status, including weather, forecasts, and airport operations @en -
ci - A Content Inventory Vocabulary https://privatealpha.com/ontology/content-inventory/1# This vocabulary defines a number of concepts peculiar to content strategy which are not accounted for by other vocabularies. @en -
tfo - Transformation Functions Ontology https://privatealpha.com/ontology/transformation/1# This document describes functions which transform HTTP representations, i.e., the actual literal payloads of HTTP messages. @en -
vas - VAS. A Semantic Model for Earth Observation Remote Sensing https://robotica.uv.es/proyectos/ASOTVAS/def/ciencia-tecnologia/vas The VAS ontological model enables the semantic integration of the heterogeneous observations used in ASOTVAS project ( https://robotica.uv.es/proyectos/ASOTVAS/ ), including ground measurements, UAV acquisitions and satellite products. Built as an extension of the W3C SOSA ontology (Janowicz et al., 2018), it incorporates a domain-specific vocabulary tailored to the needs of the Valencia Anchor Station as a CEOS LPV supersite. The model provides additional classes and properties to represent, in a homogeneous way, the different observational platforms: field sensors installed at VAS stations, UAVs equipped with multispectral cameras, and satellite missions such as Sentinel-2 and Sentinel-3. All observations follow a common SOSA pattern and share the same structure for results, units and timestamps. By aligning field, UAV and satellite observations under a unified semantic framework, the VAS ontology supports interoperable data access, consistent representation across scales, and integrated analysis of the multi-source measurements collected in ASOTVAS. @en -
saref - SAREF: the Smart Appliances REFerence ontology https://saref.etsi.org/core/ The Smart Appliances REFerence (SAREF) ontology is a shared model of consensus that facilitates the matching of existing assets (standards/protocols/datamodels/etc.) in the smart appliances domain. The SAREF ontology provides building blocks that allow separation and recombination of different parts of the ontology depending on specific needs. @en -
s4agri - SAREF extension for Agriculture https://saref.etsi.org/saref4agri/ This ontology extends the SAREF ontology for the Agricultural domain. This work has been developed in the context of the STF 534 (https://portal.etsi.org/STF/STFs/STFHomePages/STF534.aspx), which was established with the goal to create three SAREF extensions, one of them for the Agricultural domain. @en -
edifact-o - EDIFACT Ontology https://purl.org/edifact/ontology An Ontology for representing EDIFACT Messages. @en -
s4syst - SAREF4SYST: an extension of SAREF for typology of systems and their inter-connections https://saref.etsi.org/saref4syst/ The present document is the technical specification of SAREF4SYST, a generic extension of [ETSI TS 103 264 SAREF](https://www.etsi.org/deliver/etsi_ts/103200_103299/103264/02.01.01_60/ts_103264v020101p.pdf) that defines an ontology pattern which can be instantiated for different domains. SAREF4SYST defines Systems, Connections between systems, and Connection Points at which systems may be connected. These core concepts can be used generically to define the topology of features of interest, and can be specialized for multiple domains. The topology of features of interest is highly important in many use cases. If a room holds a lighting device, and if it is adjacent with an open window to a room whose luminosity is low, then by turning on the lighting device in the former room one may expect that the luminosity in the latter room will rise. The SAREF4SYST ontology pattern can be instantiated for different domains. For example to describe zones inside a building (systems), that share a frontier (connections). Properties of systems are typically state variables (e.g. agent population, temperature), whereas properties of connections are typically flows (e.g. heat flow). SAREF4SYST has two main aims: on the one hand, to extend SAREF with the capability or representing general topology of systems and how they are connected or interact and, on the other hand, to exemplify how ontology patterns may help to ensure an homogeneous structure of the overall SAREF ontology and speed up the development of extensions. SAREF4SYST consists both of a core ontology, and guidelines to create ontologies following the SAREF4SYST ontology pattern. The core ontology is a lightweight OWL-DL ontology that defines 3 classes and 9 object properties. Use cases for ontology patterns are described extensively in [ETSI TR 103 549 Clauses 4.2 and 4.3](https://www.etsi.org/deliver/etsi_tr/103500_103599/103549/01.01.01_60/tr_103549v010101p.pdf). For the Smart Energy domain: - Electric power systems can exchange electricity with other electric power systems. The electric energy can flow both ways in some cases (from the Public Grid to a Prosumer), or in only one way (from the Public Grid to a Load). Electric power systems can be made up of different sub-systems. Generic sub-types of electric power systems include producers, consumers, storage systems, transmission systems. - Electric power systems may be connected one to another through electrical connection points. An Electric power system may have multiple connection points (Multiple Winding Transformer generally have one single primary winding with two or more secondary windings). Generic sub-types of electrical connection points include plugs, sockets, direct-current, single-phase, three-phase, connection points. - An Electrical connection may exist between two Electric power systems at two of their respective connection points. Generic sub-types of electrical connections include Single-phase Buses, Three-phase Buses. A single-phase electric power system can be connected using different configurations at a three-phase bus (RN, SN, TN types). For the Smart Building domain: - Buildings, Storeys, Spaces, are different sub-types of Zones. Zones can contain sub-zones. Zones can be adjacent or intersect with other zones. - Two zones may share one or more connections. For example some fresh air may be created inside a storey if it has two controllable openings to the exterior at different cardinal points. A graphical overview of the SAREF4SYST ontology is provided in Figure 1. In such figure: - Rectangles are used to denote Classes. The label of the rectangle is the identifier of the Class. - Plain arrows are used to represent Object Properties between Classes. The label of the arrow is the identifier of the Object Property. The origin of the arrow is the domain Class of the property, and the target of the arrow is the range Class of the property. - Dashed arrows with identifiers between stereotype signs (i.e. "`<< >>`") refer to OWL axioms that are applied to some property. Four pairs of properties are inverse one of the other; the property `s4syst:connectedTo` is symmetric, and properties `s4syst:hasSubSystem` and `s4syst:hasSubSystem` are transitive. - A symbol =1 near the target of an arrow denotes that the associated property is functional. A symbol ? denotes a local existential restriction.  @en