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GS-Quant is introduced to improve Knowledge Graph Completion (KGC) by generating semantically coherent and structurally stratified discrete codes for KG entities, addressing the modality gap between continuous graph embeddings and discrete LLM tokens. It uses a Granular Semantic Enhancement module to inject hierarchical knowledge into the codebook and a Generative Structural Reconstruction module to impose causal dependencies on the code sequence. Experiments show GS-Quant outperforms existing text-based and embedding-based baselines, demonstrating its effectiveness in enabling LLMs to reason over graph structures.
Forget flat numerical compression – GS-Quant unlocks better knowledge graph completion by generating discrete codes that mirror the hierarchical nature of human reasoning.
Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human reasoning. In this paper, we propose GS-Quant, a novel framework that generates semantically coherent and structurally stratified discrete codes for KG entities. Unlike prior methods, GS-Quant is grounded in the insight that entity representations should follow a linguistic coarse-to-fine logic. We introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook, ensuring that earlier codes capture global semantic categories while later codes refine specific attributes. Furthermore, a Generative Structural Reconstruction module imposes causal dependencies on the code sequence, transforming independent discrete units into structured semantic descriptors. By expanding the LLM vocabulary with these learned codes, we enable the model to reason over graph structures isomorphically to natural language generation. Experimental results demonstrate that GS-Quant significantly outperforms existing text-based and embedding-based baselines. Our code is publicly available at https://github.com/mikumifa/GS-Quant.
