Cross-Domain Data Extraction and Knowledge Graph Construction for Dispute Analysis

This study aims to establish a comprehensive knowledge graph that spans domains and networks, with a specific focus on legal cases and their applications. The proposed methodology enables efficient collection and storage of large volumes of structured, semi-structured, and unstructured data related to cases from various sources including organizations, the government, and the internet. To analyze the relationships between roles in cases, a multimodal model is proposed to process and collect data for domain-specific knowledge graphs. Furthermore, to support social governance and public safety, a knowledge-driven intelligent recommendation algorithm is proposed in the form of question-answering, providing multiple strategies such as causal analysis, similar case matching and pre-disaster response. This work contributes to the field of artificial intelligence and natural language processing, with potential applications in legal and governmental domains, as well as in disaster response and prevention.