Behavior-Rule Inference Based on Hyponymy–Hypernymy Knowledge Tree

Behavior-rule reasoning aims to infer the corresponding applicable rules from specific behaviors and is a type of inductive reasoning that goes from special cases to general ones. This paper proposes a behavior-rule inference model based on hyponymy–hypernymy knowledge trees, which maps behaviors to corresponding rules through deep learning by processing textual behavior sequences. The primary contributions of this work are threefold: we present a systematic framework that adapts K-BERT to the legal domain by integrating domain-specific hyponymy–hypernymy knowledge trees, addressing the unique challenges of legal text understanding; we conduct comprehensive optimization of key components, including context length, loss function, and base model selection, providing empirical guidelines for applying pre-trained models to legal reasoning tasks; and we propose a practical evaluation metric (tolerance) that mimics real-world legal decision-making processes, providing extensive analysis on the effectiveness of different knowledge types in legal inference.