Can data improve knowledge graph?

Abstract

The quality of knowledge graphs (KGs) significantly influences their utility in downstream applications. Traditional methods for enhancing KG quality typically involve manual efforts and knowledge pattern learning to detect errors and complete missing triples. These approaches often incur high manual costs. To address these challenges, this paper proposes a novel “data-driven” approach to KG improvement. This method utilizes numerical data records to validate and enhance the information within KGs, overcoming limitations such as the requirement for a robust internal structure of KGs or the scarcity of expert resources. A pioneering technique that integrates Markov Boundary discovery with correlation analysis of data properties is developed in this study. This technique aims to identify and correct errors, as well as to fill in missing components of the KGs. To evaluate the effectiveness of this approach, experimental analysis was conducted, highlighting its potential to significantly improve KG accuracy and completeness. This data-driven strategy reduces reliance on extensive manual intervention and expert knowledge, introducing a scalable way to refine KGs using empirical data. The results from the experiments demonstrate the capability of this method to enhance the quality of KGs, marking it as a valuable contribution to the field of knowledge management.