Towards a knowledge graph to describe and process data defects

Authors Joao Marcelo Borovina Josko
Lisa Ehrlinger
Wolfram Wöß
Editors Fritz Laux
Lisa Ehrlinger
Title Towards a knowledge graph to describe and process data defects
Booktitle Proceedings of the 11th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019)
Type in proceedings
Publisher IARIA
ISBN 978-1-61208-715-3
Month June
Year 2019
Pages 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.