A self-supervised VQ-VAE with a GCN encoder and semantic distillation learns discrete entity codes that, when used as LLM tokens, improve link prediction and triple classification with only 16 tokens per entity.
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Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
A self-supervised VQ-VAE with a GCN encoder and semantic distillation learns discrete entity codes that, when used as LLM tokens, improve link prediction and triple classification with only 16 tokens per entity.