ABSTRACT
For a crime case, the related physical evidence and information can be termed entities, and there exist different types of relationships between entities. Entity-relationship models connect numerous entities through different relationships, which is useful in crime reconstructions. However, two types of problems may occur that can mislead crime reconstructions in the real world. Specifically, important entities may not be collected and vital relationships may go undiscovered. In this paper, we used an approach based on an entity-relationship model to address these problems. We organized the related entities used to reconstruct crimes according to their physical properties and sorted the relationships between entities through temporal, spatial and logical dimensions. The proposed approach is called 'Scenario-Entity Analysis' (SEA), and it uses several steps for discovering entities and relationships. The SEA also provides a framework for associating events/scenarios with evidence, which is important for crime reconstructions. Using a combination of SEA and Bayesian networks, a three-layered Bayesian network was constructed for uncertainty reasoning. A knife-attack case is then presented to demonstrate the analytical process of SEA.
