semwebquality.org semwebquality.org

semwebquality.org

SemWebQuality.org

Welcome to SemWebQuality.org. This website provides information and tools about data quality in Semantic Web architectures. Data Quality Constraint Library for SPIN. The data quality constraints library may be used (1) for the identification of data quality problems and (2) for quality assurance during data entry. Download DQ-Constraints in RDF. Data Quality Management Vocabulary. An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing.

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Welcome to SemWebQuality.org. This website provides information and tools about data quality in Semantic Web architectures. Data Quality Constraint Library for SPIN. The data quality constraints library may be used (1) for the identification of data quality problems and (2) for quality assurance during data entry. Download DQ-Constraints in RDF. Data Quality Management Vocabulary. An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing.
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2 from semwebquality org
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4 dq constraints library primer
5 dq constraints library documentation
6 dq constraints mailing list
7 dqm vocabulary primer
8 dqm vocabulary mailing list
9 data requirements
10 data quality management
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SemWebQuality.org | semwebquality.org Reviews

https://semwebquality.org

Welcome to SemWebQuality.org. This website provides information and tools about data quality in Semantic Web architectures. Data Quality Constraint Library for SPIN. The data quality constraints library may be used (1) for the identification of data quality problems and (2) for quality assurance during data entry. Download DQ-Constraints in RDF. Data Quality Management Vocabulary. An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing.

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Data Quality Constraints Library - Documentation

http://semwebquality.org/documentation/primer

Data Quality Constraints Library. Christian Fürber, christian@fuerber.com (c) 2010 by Christian Fürber, http:/ www.fuerber.com. This documentation is available under a Creative Commons Attribution-No Derivative Works 3.0 Germany License. You are free to use and distribute this document as long as it remains unchanged. Note that this license applies only to the documentation. The Data Quality Constraints Library itself is available under a GNU Lesser General Public License. STATUS OF THIS DOCUMENT. This d...

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The Semantic Web | Strategies for Building Semantic Web Applications

http://notes.3kbo.com/semantic-web

Strategies for Building Semantic Web Applications. Refers to the W3C’s. Vision of a Web of linked data. Complementing the well known Web of documents. The goal of the Web of linked data. Is to enable computers to do more useful work and to develop systems that can support trusted interactions over the network. The Semantic Web Standards. Supporting the Web of linked data. These standards build on a core Web Architecture. And enable people to create data stores on the Web, build vocabularies ( ontologies.

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Wiki of the E-Business and Web Science Research Group

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From Wiki of the E-Business and Web Science Research Group. Public Wiki of the E-Business and Web Science Research Group. Diploma / Master / Bachelor Theses. Public Wiki of the E-Business and Web Science Research Group. This is the public wiki of the E-Business and Web Science Research Group at Bundeswehr University Munich. Is at http:/ www.unibw.de/ebusiness/. Visit http:/ www.ebusiness-unibw.org/. To get a list of other content and services on this server. Funding for SMEs / Fördermittel für KMUs.

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Welcome to SemWebQuality.org. This website provides information and tools about data quality in Semantic Web architectures. Data Quality Constraint Library for SPIN. The data quality constraints library may be used (1) for the identification of data quality problems and (2) for quality assurance during data entry. Download DQ-Constraints in RDF. Data Quality Management Vocabulary. An Ontology for Data Requirements Management, Data Quality Monitoring, Data Quality Assessment, and Data Cleansing.

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