Bingcong Xue’s paper “Knowledge Graph Quality Management: a Comprehensive Survey” has been accepted by TKDE 2022.

Knowledge graph (KG) expresses real-world entities and relationships in a structural way and has become the cornerstone of artificial intelligence technologies, which is widely used in downstream tasks like question answering and recommend systems. However, quality issues such as incomplete, inaccurate and inconsistent, usually lie in existing KGs.  While on the other hand, research on data quality has a long history, with a large number of methods and tools developed. More and more researchers turn eyes to the quality issues of knowledge graphs in recent years, and many graph-specific quality management methods are proposed.

This paper provides a comprehensive review of quality management on knowledge graphs, covering overall research topics about not only quality issues, dimensions and metrics, but also quality management processes from quality assessment and error detection, to error correction and KG completion. Existing works are categorized by various aspects including used methods, target dimensions and goals. And in the end, we discuss some key issues and possible directions on knowledge graph quality management for further research.