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  • The initiative Aragón Open Data was initiated by agreement of 17 of July of 2012 of the Government of Aragon. Under the same was ordered the start of the project to open public data and on February 6, 2013 was implemented through the Portal <a href="http://opendata.aragon.es/"> opendata.aragon.es </a>. Throughout this time there have been numerous works to achieve automation in the publication of information to ensure that third parties can reuse it in the best way. Given the volume of data that begins to exist, within the line of work of automation in information management, all those elements that help in the improvement of the <b> structuring of information </b> and the <b> standardization of the data </b> contained in the databases are beginning to have a special relevance. Based on this, within the General Directorate of Electronic Administration and Information Society, the idea arises of generating a set of technical and legal rules that allow to deepen in that standardization and that lead to think in the creation of the Interoperable Information Scheme Of Aragon (E2IA). The E2IA thus emerges as the framework in which the open data and in general the information of the Government of Aragon can begin to be automated in a much more profound way. The E2IA has to have a number of technical, organizational and legal elements that need to be developed. For this reason, the Technological Institute of Aragon (ITAINNOVA) has been entrusted with carrying out actions consisting in identifying, studying and analyzing current research trends and technological development in relation to ontologies and dictionaries of data interoperability, defining the ontological proposal, performing The necessary tests to validate the ontological proposal and generate the text and web versions of the ontology. @en
  • This vocabulary defines a number of concepts peculiar to content strategy which are not accounted for by other vocabularies. @en
  • Defines concepts related to the structure of the US National Airspace System (NAS) @en
  • A small ontology to model supply networks (supply chains) from all industries through products that are interlinked based on derivational dependencies. @en
  • 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
  • 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. ![SAREF4SYST overview](diagrams/overview.png) @en
  • This ontology extends the SAREF ontology for the water domain. This work has been developed in the context of the STF 566, which was established with the goal to create three SAREF extensions, one of them for the water domain. @en
  • 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
  • This ontology extends the SAREF ontology for the environment domain, specifically for the light pollution domain, including concepts like photometers, light, etc. @en
  • GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
  • GConsent provides concepts and relationships for defining consent and its associated information or metadata with a view towards GDPR compliance. It is the outcome of an analysis of consent and requirements associated with obtaining, using, and changes in consent as per the GDPR. The ontology also provides an approach to using its terms in various scenarios and use-cases (see more information in the documentation) which is intended to assist in its adoption. @en
  • Ontology that defines concepts for representing the aerOS Data Catalog @en
  • Metadata vocabularies are used in various domains of study. It provides an in-depth description of the resources. In this work, we develop Algorithm Metadata Vocabulary (AMV), a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). The snag faced by the researchers in the current time is the failure of getting relevant results when searching for algorithms in any search engine. AMV is represented as a semantic model and produced OWL file, which can be directly used by anyone interested to create and publish algorithm metadata as a knowledge graph, or to provide metadata service through SPARQL endpoint. To design the vocabulary, we propose a well-defined methodology, which considers real issues faced by the algorithm users and the practitioners. The evaluation shows a promising result. @en
  • A vocabulary to represent relations that should be more transparent, usually between powerfull people or institutions @en
  • The Construction Dataset Context (CDC) ontology is an extension of DCAT v2.0, a W3C Recommendation ontology for describing (RDF and non-RDF) datasets published on the Web. Using this extension, it becomes possible to describe a context for construction-related datasets that are being distributed using Web technology as well as datasets that are not shared outside an organization such as local copies, work in progress and other datasets that remain internal. This dataset metadata encompasses the temporal context (period or snapshot), the type of content of the dataset (as-built, design, etc.) and relations between contextualized datasets (previous as-built, requirements related to a design, etc.). In addition, this DCAT extension also provides terminology for managing dataset distributions that are scoped to a certain (named or default) graph of an RDF file or quadstore. @en