Robust Knowledge Graph Cleaning
Maximilian K. Egger, Davide Mottin, Panagiotis Karras · Scopus (Elsevier) · 2025
Data quality is needed to properly and reliably use the information represented in the dataset. The increasing volume of data renders data preparation and cleaning increasingly difficult. Additionally, more diverse types of data structures for databases, like graphs, get used and need to be handled differently. This leads to the necessity of robust methods to increase data integrity, scalable approaches for finding and fixing errors, and local-oriented algorithms that can be used to pinpoint attention where needed. In my PhD project, I focus mainly on knowledge graph structures and define and establish different tools that can be used to clean the knowledge graphs.