Evaluation approaches of personal knowledge graphs

Knowledge graphs (KGs) provide structured data for users' applications such as recommendation systems, personal assistants, and question-answering systems. The quality of the underlying applications relies deeply on the quality of the knowledge graph. However, KGs inevitably have inconsistencies, such as duplicates, wrong assertions, and missing values. The presence of such issues may compromise the outcome of business intelligence applications. Hence, it is crucial and necessary to explore efficient and effective methods for tackling the evaluation of KGs. These techniques get much tougher when the KG deals with personal data, which is referred to as Personal Knowledge Graph (PKG). This chapter covers PKG's creation, population, and more importantly their evaluation from a data quality perspective.