Error Detection in Knowledge Graphs
Abstract
Knowledge graphs (KGs) have been widely applied as an efficient tool in storing information in the digital age. They always contain a considerable number of errors and could significantly affect downstream tasks. To tackle this issue, developing generalizable error detection algorithms on KGs is needed. However, it is still challenging due to the unique data characteristics of KGs. In this talk, I will present my work that could learn both the sequential information within the triples and contextual information for KG error detection.
Citation
Liu, Yezi (2021). Error Detection in Knowledge Graphs. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /195053.