Method for automatic extracting process models from criminal case records with business process model

Nowadays, a large amount of process information is hidden in natural language documents. Extracting process models from documents would provide clear and concise views of them. There exists lots of related research in different application domains. While handling the Chinese Judgement Document, where the process of handling a legal case is recorded, we found that challenges features: (1) the events do not strictly follow chronological order;(2) a mass of noisy information exists. Although these features are widely found in texts in a variety of fields, little work is done to deal with them. A portable approach for mining process models was proposed from texts with these features. Through prior domain knowledge and machine learning, a novel data entity named event framework was constructed to solve above difficulties and generate business process models automatically. Experimental results showed that the proposed approach could effectively handle the above-mentioned challenges. © 2024 CIMS. All rights reserved.