4 citations · 4 across the 5 of their papers we have counts for
6 papers
Implementation Guidelines for Data Quality Metrics
Philipp Jung, Katinka Becker, Felix Biessmann +4
Despite decades of data quality (DQ) research, a gap remains between DQ dimensions, such as accuracy or completeness, which the literature defines in textual form, and DQ tools, wh…
WiDiff: Extracting Changes from Wikidata's Edit History
Carolina Cortés, Lisa Ehrlinger, Lorena Etcheverry +1
Knowledge graphs have become a key resource for integrating heterogeneous data and powering downstream tasks such as question answering, entity linking, and semantic search. They a…
Data Quality Rule Generation with LLMs
Anna-Christina Glock, Thomas Hütter, Johannes Fürnkranz +3
The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For example, a wrong drug unit…
A Catalog of Data Errors
Divya Bhadauria, Hazar Harmouch, Felix Naumann +2
Data errors are widespread in real-world databases and severely impact downstream applications, such as machine learning pipelines or business analytics reports. Causes of such err…
Is SHACL Suitable for Data Quality Assessment?
Carolina Cortés, Lisa Ehrlinger, Lorena Etcheverry +1
Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata, to represent domain knowledge and support artif…
Data Quality Assessment: Challenges and Opportunities
Sedir Mohammed, Lisa Ehrlinger, Hazar Harmouch +2
Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such a…