DQM-Vocabulary Primer
From SemWebQuality.org
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| + | == Purpose and Usage of the DQM-Vocabulary == | ||
| + | The DQM-Vocabulary was created to support data quality management acitivities in [[Wikipedia:Semantic Web|Semantic Web]] architectures. It's major stregth is the ability to represent data requirements, i.e. quality-relevant expectations, so that computers can interpret the requirements and take further actions. Among other things, the DQM-Vocabulary enables the following features: | ||
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| + | * Structured representation of data requirements | ||
| + | * Annotation of data quality assessment scores and data quality problem reports to data elements | ||
| + | * Exchange of data quality information and data requirements on Web-scale | ||
| + | * | ||
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| + | facilitates the structured representation of data requirements, assessment results, data cleansing rules, and data quality problems connected to the accordant conceptual elements. The ontology enables data quality monitoring, data quality assessment, and data cleansing based on quality-relevant knowledge represented through this ontology. | ||
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== Examples == | == Examples == | ||
* [[Creating Data Requirements]] | * [[Creating Data Requirements]] | ||
Revision as of 11:49, 9 September 2011
Purpose and Usage of the DQM-Vocabulary
The DQM-Vocabulary was created to support data quality management acitivities in Semantic Web architectures. It's major stregth is the ability to represent data requirements, i.e. quality-relevant expectations, so that computers can interpret the requirements and take further actions. Among other things, the DQM-Vocabulary enables the following features:
- Structured representation of data requirements
- Annotation of data quality assessment scores and data quality problem reports to data elements
- Exchange of data quality information and data requirements on Web-scale
facilitates the structured representation of data requirements, assessment results, data cleansing rules, and data quality problems connected to the accordant conceptual elements. The ontology enables data quality monitoring, data quality assessment, and data cleansing based on quality-relevant knowledge represented through this ontology.
