Towards a knowledge graph to describe and process data defects

Autoren Joao Marcelo Borovina Josko
Lisa Ehrlinger
Wolfram Wöß
Editoren Fritz Laux
Lisa Ehrlinger
Titel Towards a knowledge graph to describe and process data defects
Buchtitel Proceedings of the 11th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019)
Typ in Konferenzband
Verlag IARIA
ISBN 978-1-61208-715-3
Monat June
Jahr 2019
Seiten 57-60
SCCH ID# 19017
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.