This fusion mechanism effectively enhances the model’s ability to learn node representations, especially in complex graph structures, with accuracy improvements of 1%-2% compared to GCN and GraphSAGE.

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our ...

Abstract: Contrastive graph ... cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops.

However, domain-specific approaches for extracting knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to ...