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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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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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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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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
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The nanopub ontology @en
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Creative Commons Ontology, extending RDF file at http://creativecommons.org/schema.rdf @en
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semiotics- A content ontology pattern that encodes a basic semiotic theory, by reusing the situation pattern.A content ontology pattern that encodes a basic semiotic theory, by reusing the situation pattern. The basic classes are: Expression, Meaning, Reference (the semiotic triangle), LinguisticAct (for the pragmatics), and Agent. A linguistic act is said to be context for expressions, with their meanings and references, and agents involved. Based on this pattern, several specific linguistic acts, such as 'tagging', 'translating', 'defining', 'formalizing', etc. can be defined, so constituting a formal vocabulary for a pragmatic web. @en
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The Data Knowledge Vocabulary allows for a comprehensive description of data assets and enterprise data management. It covers a business data dictionary, data quality management, data governance, the technical infrastructure and many other aspects of enterprise data management. The vocabulary represents a linked data implementation of the Data Knowledge Model which resulted from extensive applied research. @en
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OWL pattern for describing activity models as abstract dependencies among classes. @en
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An extension of W3C VoID that is able to represent these metrics for expressing the Connectivity Metrics of a Semantic Warehouse. @en
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The Open NEE Model defines an extension of the Open Annotation Data Model (http://www.openannotation.org/spec/core) that allows describing in RDF the result of a Named Entity Extraction (NEE) process, enabling thereby an application to run advanced (SPARQL) queries over the annotated data. The model also exploits the Open NEE Configuration Model (http://www.ics.forth.gr/isl/oncm) for relating the output of a NEE process with an applied configuration (serving provenance information to the output of the entire NEE process). @en
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The Open NEE Configuration Model defines a Linked Data-based model for describing a configuration supported by a Named Entity Extraction (NEE) service. It is based on the model proposed in "Configuring Named Entity Extraction through Real-Time Exploitation of Linked Data" (http://dl.acm.org/citation.cfm?doid=2611040.2611085) for configuring such services, and allows a NEE service to describe and publish as Linked Data its entity mining capabilities, but also to be dynamically configured. @en
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CiteDCAT-AP is an extension of the DCAT application profile for data portals in Europe (DCAT-AP) for describing resources documented by using the DataCite metadata schema - the de facto standard for data citation, and used across scientific disciplines. Its basic use case is to make research data searchable on general data portals, thereby bridging the gap between scientific and public sector information. For this purpose, CiteDCAT-AP provides an RDF vocabulary and the corresponding RDF syntax binding for the metadata elements defined in DataCite. @en