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ISO 37120 – Sustainable Development and Resilience of Communities – Indicators for City Services and Quality of Life (under TC268) http://ontology.eil.utoronto.ca/ISO37120.html This OWL file defines a class for each indicator defined in the ISO 37120 standard. Names for each indicator are provided. Text definitions are provided only for Economy, Education and Energy indicators, due to copyright restrictions imposed by ISO. This file is meant to provide a single URI for each indicator. An ontology for representing an indicator's supporting data plus meta information such as provenance, validity and trust can be found in: http://ontology.eil.utoronto.ca/GCI/Foundation/GCI-Foundation.owl Documentation of the ontology can be found in: http://eil.utoronto.ca/smartcities/papers/GCI-Foundation-Ontology.pdf @en
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This ontology provides basic classes and more detailed properties for representating international street addresses, phone numbers and emails. Rather than using existing ontologies, such as vcard, it was decided to create a new one as the vcard and foaf ignore the details of international addresses, phone numbers, etc. @en
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Utility concepts for everyday life @en
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This ontology models and represents vCards in RDF using current best practices @en
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The Dataset Usage Vocabulary (DUV) is used to describe consumer experiences, citations, and feedback about datasets from the human perspective. @en
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The Person Core Vocabulary provides a minimum set of classes and properties for describing a natural person @en
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The Registered Organization Vocabulary is a profile of the Organization Ontology for describing organizations that have gained legal entity status through a formal registration process, typically in a national or regional register. @en
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FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. @en
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This document specifies a vocabulary for describing an IBIS (issue-based information system). @en
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This ontology models personalized tourist experiences by representing cities, points of interest, events, accommodations, restaurants, transportation, and their relationships. This ontology is part of a university project. @en
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The development of the SAREF4GRID ontology has been partially funded by the IA4TES project (MIA.2021.M04.0008), funded by the Spanish Ministry of Economic Affairs and Digital Transformation and by the NextGenerationEU program @en
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The Crime Event Model is an ontology for the representation of crime events extracted from local newspapers. It could be employed for Crime Analysis purposes: extracting crime information from newspapers and enriching them with proper machine-readable semantics is a critical task to help law enforcement agencies at preventing crime, supporting criminal investigations and evaluating the action of law enforcement agencies themselves. The model is based on the fundamental 5W1H journalistic questions, that are Who?, What?, When?, Where?, Why? and How?. Another important requirement was the attempt to exploit existing knowledge graphs and ontologies such as the Simple Event Model (SEM) Ontology and the Schema.org data model for interoperability and interconnection. @en
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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
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GDPRov is an OWL2 ontology to express provenance metadata of consent and data lifecycles towards documenting compliance for GDPR. @en
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The General Data Protection Regulation (GDPR) is comprised of several articles, each with points that refer to specific concepts. The general convention of referring to these points and concepts is to quote the specific article or point using a human-readable reference. This ontology provides a way to refer to the points within the GDPR using the EurLex ontology published by the European Publication Office. It also defines the concepts defined, mentioned, and requried by the GDPR using the Simple Knowledge Organization System (SKOS) ontology. @en