GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
The recently introduced GS-Quant framework tackles the disparity between continuous graph embeddings and discrete LLM tokens in Knowledge Graph Completion (KGC). In contrast to earlier quantization techniques that view quantization merely as a form of numerical compression, GS-Quant produces discrete codes for KG entities that are both semantically coherent and structurally layered. It features a Granular Semantic Enhancement module that incorporates hierarchical knowledge into the codebook, allowing initial codes to represent broad semantic categories while subsequent codes focus on finer details. This method reflects the hierarchical characteristics of human reasoning. The research paper can be found on arXiv with the identifier 2604.21649.
Key facts
- GS-Quant is a framework for Knowledge Graph Completion.
- It bridges the modality gap between graph embeddings and LLM tokens.
- Prior quantization methods produce semantically entangled codes.
- GS-Quant generates semantically coherent and structurally stratified codes.
- The Granular Semantic Enhancement module injects hierarchical knowledge.
- Earlier codes capture global categories; later codes refine specifics.
- The approach mirrors human reasoning's coarse-to-fine logic.
- The paper is on arXiv with ID 2604.21649.
Entities
Institutions
- arXiv