MRNBOX: Ripple Diffusion Meets Geometric Reasoning for Taxonomy Completion

The Taxonomy Completion (TC) task aims to ffnd optimal insertion positions for new concepts under hypernym-hyponym constraints, but faces three challenges: sparse descriptions and polysemy weaken semantic embeddings with no quantiffable parent-priority mechanism; traditional GNNs miss path-dependent decay in hierarchical correlations; and global traversal is inefffcient with invalid candidate interference. To address these, we propose MRNBOX, which resolves semantic sparsity via adaptive embeddings fusing conceptual descriptions, average path lengths, and topological association features; models hierarchical structures via ripple diffusion and sparse Mixture-of-Experts (MoE) for dynamic neighbor screening; and performs two-stage geometric reasoning, where box embedding exclusion ffrst prunes irrelevant branches before ffne-grained matching reffnes positioning.This enables efffcient semantic-structural collaborative modeling for dynamic taxonomy maintenance.