From Data to Evidence: Multi-Source Evidence Association Model Based on Bayesian Network

Abstract

Recent years have witnessed the boom of artificial intelligence. With the advancement of judicial information, using artificial intelligence (AI) technologies to mine judicial big data is of great significance in smart courts. However, the reasoning of the evidence chain mainly relies on the judge’s manual work in the litigation process. How to model the multi-source evidence association (MSEA) and reason credible evidence chains (CEC) automatically is largely unexplored. In this paper, we propose an MSEA model based on the Bayesian network. Firstly, we construct an MSEA network in which each evidence element serves as a node, and the node correlation probability is calculated via the association relationship among the evidence elements. Subsequently, with the guidance of the event judgment chain, the MSEA model is constructed based on Bayesian networks. In the end, we use a genetic algorithm to optimize the Bayesian network and select credible evidence chains. To the best of our knowledge, this is the first time that using a probability graph model to mining the association of multi-source evidence. Experiments and the case analysis prove the effectiveness of our method.