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An Ontology for representing EDIFACT Messages. @en
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The AIRS Linked Open Vocabulary is a way to describe human services information and referral (I&R) concepts. AIRS is the Alliance of Information and Referral Services (airs.org), which possesses over 1,000 member agencies primarily in the United States and Canada. The AIRS LOV is based on the AIRS XML Schema, available at: https://airs-xml.googlecode.com/hg/tags/3.1/airs.xsd @en
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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
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This is the extension of SAREF for the EEBus and Energy@Home project. The documentation of SAREF4EE is available at http://ontology.tno.nl/SAREF4EE_Documentation_v0.1.pdf. SAREF4EE represents 1) The configuration information exchanged in the use case 'Remote Network Management' according to the EEBus Technical Report, Protocol Specification- Remote Network Management, version 1.0.0.2, 2015-09-19; 2) The scheduling information about power sequences exchanged in the use cases Appliance scheduling through CEM and remote start' and 'Automatic cycle rescheduling', according to the message structures described in General Message Structures, version 0.1.1, 2015-10-07; 3) The monitor and control information exchanged in the use case 'Communicate appliance status and info on manually planned cycles', according to the monitoring and control part of the Energy@Home Data Model, version 1.0; and 4) the event-based data exchanged in the use case 'Demand Response', according to General Message Structures, version 0.1.1, 2015-10-07. @en
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The eccenca Publish-Subscribe Vocabulary defines concepts and relations to create statements about publishers, subscribers and their subscriptions in a Publish-Subscribe environment based on the PubSubHubbub Core 0.4 specification. @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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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
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Ontology for the orchestration of the aerOS continuum. @en
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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
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The Core module represents general-purpose concepts orthogonal to the whole network, which are imported by all other ontology modules (e.g. part-whole relation, classification). @en
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This specification describes MOD, a metadata vocabulary to describe and publish ontologies @en
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The purpose of VAEM is to provide, by import, a foundation for commonly needed resources when building an ontology. @en
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LinkLion - the Link Discovery Portal @en
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MADS/RDF (Metadata Authority Description Schema in RDF) is a knowledge organization system (KOS) designed for use with controlled values for names (personal, corporate, geographic, etc.), thesauri, taxonomies, subject heading systems, and other controlled value lists @en
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pimsii- Physicalistic Interpretation of Modelling and Simulation - Interoperability Infrastructure (PIMS-II)The Physicalistic Interpretation of Modelling and Simulation - Interoperability Infrastructure (PIMS-II) is a mid-level ontology with a focus on documenting cognitive processes and epistemic metadata @en