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Quality metrics can be (in principle) calculated on various forms of data (such as datasets, graphs, set of triples etc...). This vocabulary allow the owner/user of such RDF data to calculate metrics on multiple (and different) resources. @en
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The FIESTA-IoT ontology takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 ontology), WGS84, IoT-lite, Time, and DUL ontology. @en
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The Modular and Unified Tagging Ontology (MUTO) is an ontology for tagging and folksonomies. It is based on a thorough review of earlier tagging ontologies and unifies core concepts in one consistent schema. It supports different forms of tagging, such as common, semantic, group, private, and automatic tagging, and is easily extensible. @en
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M3 lite taxonomy is designed for the FIESTA-IOT H2020 EU project. We refactor, clean and simplify the M3 ontology designed by Eurecom (Amelie Gyrard). M3 ontology lite is currently aligned with the quantity taxonomy used by several testbeds: SmartSantander (Spain), University of Surrey (United Kingdom), KETI (Korea) and Com4Innov (France). @en
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OLiA Annotation Model for Uby Parts of Speech (Gurevych et al, 2012) extracted from the Uby DTD (http://purl.org/olia/ubyCat.owl, version of Nov 21th, 2012). References Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M. Meyer and Christian Wirth, 2012, Uby - A Large-Scale Unified Lexical-Semantic Resource, Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), Avignon, France. The DTD is made available under a Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license which is available at http://creativecommons.org/licenses/by-sa/3.0/ You are free to share (copy, distribute and transmit) the work, to develop your own extensions (adapt, remix) of the work, and to make commercial use of the work. @en
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This vocabulary defines a set of terms for describing changes to resource descriptions. @en
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SCOT is an ontology for describing the structure and the semantics for tagging data across heterogenous users, sources, and applications. @en
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The Databugger ontology describes concepts used in Databugger, a test driven data-debugging framework that can run automatically generated (based on a schema) and manually generated test cases against an endpoint. @en
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This ontology is a version of the ISO TC211, Group for Ontology Management (GOM)'s OWL ontology interpretation of the ISO19160-1:2015 "Addressing -- Part 1: Conceptual model" standard (see https://www.iso.org/standard/61710.html) taken from that ontology's source code, published at https://github.com/ISO-TC211/GOM/tree/master/isotc211_GOM_harmonizedOntology/19160-1/2015. @en
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Our goal is to significantly improve the data mobility between all stakeholders by providing a standardized vocabulary using Semantic Web technologies and ontologies. For the open vocabulary covering various mobility aspects we use RDF (Resource Description Framework) - a recommended specification of the World Wide Web Consortium (W3C) and the so-called lingua franca for the integration of data and web. We invite everyone who is interested to join our MobiVoc initiative and to participate in the development of the Open Mobility Vocabulary. @en
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A vocabulary that supports the publication of Open Data by providing the means to capture machine-readable "rights statements", e.g. the licensing information, copyright notices and attribution requirements that are associated with the publication and re-use of a dataset. @en
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This RDF document contains a library of data quality constraints represented as SPARQL query templates based on the SPARQL Inferencing Framework (SPIN). The data quality constraint templates are especially useful for the identification of data quality problems during data entry and for periodic quality checks during data usage. @en
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Ontology Specialties describes all possible specialties (directions) in the RF, in which the UGNS they are composed, as well as information about their old codes / groups / names. @en
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The Data Value Vocabulary (DaVe) is an extensible core vocabulary that allows user to use custom data value dimensions and metrics to characterise data value in a specific context. This flexibility allows for the comprehensive modelling of data value. As a data value model, DaVe allows users to monitor data value as it occurs within a data exploitation or value creation process (data value chain) @en
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TheSPITFIRE Ontology (spt) is based on the alignment among Dolce+DnS Ultralite(dul), the W3C Semantic Sensor Network ontology (ssn) and the Event Model-F ontology (event). @en