Title: FHDM-KGE: Fuzzy Hierarchical Modeling and Dual Mixture-of-Experts for Knowledge Graph Embedding
Primary area: learning on graphs and other geometries & topologies
Keywords: Knowledge graph embedding, fuzzy hierarchical modeling, dual mixture of experts

Abstract: Real world knowledge graphs (KGs) exhibit rich hierarchical structures, and effectively modeling such structures is crucial for learning high-quality representations and boosting downstream reasoning performance. However, existing hierarchy-aware KGE methods suffer from two key limitations: (i) hard layer assignment inevitably causes information loss for boundary or multi-role entities, and (ii) the neglect of relational cross-layer differences restricts the expressiveness of relation embeddings. To overcome these issues, we propose FHDM-KGE, a Fuzzy Hierarchical Modeling with Dual Mixture-of-Experts framework for knowledge graph embedding (KGE). First, we introduce a differentiable SpringRank-based fuzzy hierarchy that assigns entities to multiple layers with soft memberships, preserving multi-level semantics. Then, we design a dual MoE architecture: an entity-side MoE (EMoE) module gated by fuzzy memberships to capture intra-layer nuances, and a relation-side MoE (EMoE) module guided by head–tail hierarchical differences to model cross-layer relational patterns. The resulting entity and relation embeddings are scored with a ConvE decoder. Experiments on multiple public benchmarks demonstrate that FHDM-KGE consistently outperforms strong baselines, validating the effectiveness of combining fuzzy hierarchical modeling with dual MoE specialization.