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An ontology for natural language terms description, including scripts, languages and meanings. The Lexvo.org ontology is still under development and may not be able to address all needs. Please also consider using the Lingvoj Ontology and the GOLD ontology, whereever appropriate. @en
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The Linked Earth Ontology aims to provide a common vocabulary for annotating paleoclimatology data @en
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An OWL vocabulary to include and exploit probabilistic information in SHACL validation reports @en
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Global City Indicator Foundation Ontology developed by the Information Engineering Group, Mechanical & Industrial Engineering, University of Toronto. Contains the foundation ontologies required to represent ISO 37120 city indicators, including Placenames, Time, Measurement, Provenance, Statistics, Validity and Trust. See: Fox, M.S., (2013), "A Foundation Ontology for Global City Indicators", Global City Institute Working Paper, Vol. 1, No.4, pp. 1-45. Global Cities Institute, University of Toronto. Updated 24 June 2014: http://www.eil. Based on the Global City Indicators Facility, University of Toronto: http://www.cityindicators.org/Deliverables/Core%20and%20Supporting%20Indicators%20Table%20SEPTEMBER%202011.pdf. Contact: Mark S. Fox, msf@eil.utoronto.ca @en
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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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Open 311 Ontology This ontology generalizes the concepts that appear in 311 open data files published by several cities (Toronto, New York, Chicago, Vancouver) across North America. It provides a generis representation of 311 data that other cities can map their data onto and be used as a means of achieving interoperability. @en
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This ontology defines concepts related to federation of internet infrastructures. @en
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The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any 'thing', whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. @en
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The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. @en
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This document specifies a vocabulary for asserting the existence of official endorsements or certifications of agents, such as people and organizations. @en
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This ontology is a reduced-in-scope version of the [W3C Decisions and Decision-Making Incubator Group](https://www.w3.org/2005/Incubator/decision/)'s Decision Ontology (DO) which can be found at <https://github.com/nicholascar/decision-o>. It has been re-worked to align entirely with the W3C's [PROV ontology](https://www.w3.org/TR/prov-o/) since it is widely recognised that analysing the elements of decisions *post hoc* is an exercise in provenance. Unlike the original DO, this ontology cannot be used for *normative* scenarios: it is only capable of recording decisions that have already been made (so-called *data-driven* use in the DO). This is because PROV, to which this ontology is completely mapped, does not have a templating system which can indicate what *should* occur in future scenarios. This ontology introduces only one new element for decision modelling over that which was present in the DO: an Agent which allows agency in decision making to be recorded. @en
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This is a vocabulary for modeling jobs offer in Spain. @en
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The Data Quality Management Vocabulary - An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing @en
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An ontology to describe competences and human capabilities @en
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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