Towards a knowledge graph to describe and process data defects
Joao Marcelo Borovina Josko
|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)|
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.