Anomaly Detection for Numerical Literals in Knowledge Graphs: A Short Review of Approaches
Farshad Bakhshandegan Moghaddam, Jens Lehmann, Hajira Jabeen · 2023
Anomaly Detection is an important problem that has been well-studied within diverse research areas and application domains. However, within the field of Semantic Web and Knowledge Graphs, anomaly detection has been relatively overlooked. Additionally, the existing literature on anomaly detection over Knowledge Graphs lacks proper organization and poses challenges for new researchers seeking a comprehensive understanding. In light of these gaps, this paper aims to offer a well-structured and comprehensive overview of the existing research conducted on anomaly detection over Knowledge Graphs. In this overview, we review the quality metrics of KGs and discuss the possible errors which may occur in different parts of the RDF data. Additionally, we outline a generic conceptual framework for the execution pipeline of Anomaly Detection over KGs. Moreover, we study the anomaly detection techniques, along with their variants, and present key assumptions, to differentiate between normal and anomalous behavior. Finally, we outline open issues in research and challenges encountered while adopting anomaly detection techniques for KGs.