Towards a knowledge graph to describe and process data defects

J. Borovina Josko, L. Ehrlinger, W. Wöß. Towards a knowledge graph to describe and process data defects. pages 57-60, 6, 2019.

Autoren
  • Joao Marcelo Borovina Josko
  • Lisa Ehrlinger
  • Wolfram Wöß
BuchProceedings of the 11th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019)
TypIn Konferenzband
VerlagIARIA
ISBN978-1-61208-715-3
Monat6
Jahr2019
Seiten57-60
Abstract

The reliability and trustworthiness of machine learning models depends directly on the data used to train them. Knowledge about data defects that affect machine learning models is most often considered implicitly by data analysts, but usually no centralized data defect management exists. Knowledge graphs are a powerful tool to capture, structure, evolve, and share semantics about data defects. In this paper, we present an ontology to describe data defects and demonstrate its applicability to build a large public or enterprise knowledge graph.