An interpretable knowledge recommendation method for civil dispute mediation

Abstract

The demand for efficient and fair civil dispute mediation is driving the development of intelligent knowledge recommendation technologies. However, existing approaches face challenges in interpretability and reasoning over complex relationships. This study proposes an Interpretable Knowledge Recommendation Method (IKRM) that integrates deep learning and multi-hop reasoning to provide precise and transparent decision support for online mediation platforms. First, to address the extraction of specialized terms and intricate relationships in legal texts, we propose a pre-trained model-based semi-joint extraction method combined with ontology design, constructing a civil dispute knowledge graph that enables hierarchical semantic modeling of legal concepts. Second, we design a hybrid multi-hop reasoning framework that combines neural logic programming for numerical rule-based latent relation mining and cognitive graphs for multi-path reasoning, dynamically generating traceable explanations during path expansion. IKRM performs better than mainstream baseline models in terms of all key evaluation indicators, according to experiments validated using multi-source Chinese legal datasets. It additionally exhibits greater reasoning robustness for difficult queries. This study creates a new paradigm for legal knowledge recommendation systems that is modular, interpretable, and effective. It also contributes to larger social equitable governance by offering accurate decision assistance for civil dispute mediation in China.