A Heterogeneous Graph Based on Legal Documents and Legal Statute Hierarchy for Chinese Legal Case Retrieval

Legal case retrieval, which aims at retrieving similar legal cases based on the given query cases, plays an important role in intelligent legal systems. Existing methods including text-based and network-based methods have achieved notable advancements. However, text-based methods tend to overlook the significance of legal statutes and network-based information. Network-based methods ignore the rich text-based information in legal documents and are not applicable when legal citation networks are sparse. Moreover, most of the existing methods cannot be applied to inductive situations where new query cases continue to emerge. To overcome these issues, we propose a heterogeneous graph based on legal documents and legal statute hierarchy called LeDSGra, and utilize a heterogeneous graph representation learning method to combine both text-based and network-based information. Additionally, we design a module to identify relevant legal articles for query cases to connect the query cases to the existing citation network whereby the LeDSGra can be applied in inductive situations. Extensive experiments have been conducted on two real-world legal datasets which are LeCaRD and ELAM, and the experimental results demonstrate that our proposed method significantly improve the performance on the task of legal case retrieval. Moreover, our model outperforms other strong baseline models on both two datasets.