Quality assessment of knowledge graph hierarchies using KG-BERT

Knowledge graphs in both public and corporate settings need to keep pace with the constantly growing amount of data being generated. It is, therefore, crucial to have automated solutions for assessing the quality of Knowledge Graphs, as manual curation quickly reaches its limits. This research proposes the use of KG-BERT for a triple (binary) classification task that assesses the quality of a Knowledge Graphs’s hierarchical structure. The use of KG-BERT allows the textual as well structural aspects of a Knowledge Graph to be leverage for this quality assessment (QA) task. The performance of our proposed approach is measured using four different Knowledge Graphs: two branches (Physics and Mathematics) of a corporate Knowledge Graph - OmniScience, a WordNet subset, and the UMLS Semantic Network. Our method yields high-performance scores on all four KGs (88-92% accuracy) making it a relevant tool for quality assessment and knowledge graph maintenance.