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Towards a Knowledge Graph to Describe and Process Data Defects

Authors:
João Marcelo Borovina Josko
Lisa Ehrlinger
Wolfram Wöß

Keywords: Data Defects; Data Quality Assessment; Knowledge Graphs

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.

Pages: 57 to 60

Copyright: Copyright (c) IARIA, 2019

Publication date: June 2, 2019

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-715-3

Location: Athens, Greece

Dates: from June 2, 2019 to June 6, 2019