Revealing One-to-Many Event Relationships in Event Knowledge Graphs

Object-centric process mining is recognized to overcome the limitations of traditional process mining by offering approaches for the analysis of processes with multiple case notions such as collaborations. Event knowledge graphs are an effective tool for gathering, manipulating, and visualizing event and entity relations. Current approaches focus on inferring correlations between events and objects and directly-follows relationships between events correlated to the same object. However, object-to-object relations may hide one-to-many relations between events essential for understanding the actual flow among processes. We propose an approach to reveal these one-to-many causal relationships in an event knowledge graph. By defining when two events are causally related and extending the standard approach of event knowledge graphs construction to reveal them. We assess the approach using two case studies.