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Extending functional dependency to detect abnormal data in rdf graphs

Extending functional dependency to detect abnormal data in rdf graphs

This video was recorded at 10th International Semantic Web Conference (ISWC), Bonn 2011. Data quality issues arise in the Semantic Web because data is created by diverse people and/or automated tools. In particular, erroneous triples may occur due to factual errors in the original data source, the acquisition tools employed, misuse of ontologies, or errors in ontology alignment. We propose that the degree to which a triple deviates from similar triples can be an important heuristic for identifying errors. Inspired by functional dependency, which has shown promise in database data quality research, we introduce value-clustered graph functional dependency to detect abnormal data in RDF graphs. To better deal with Semantic Web data, this extends the concept of functional dependency on several... Show More

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