Fig. 1 | Scientific Reports

Fig. 1

From: Contextual semantics graph attention network model for entity resolution

Fig. 1

CSGAT Workflow Diagram: Starting with the generation of initial embeddings using GloVe, followed by hierarchical graph partitioning. BERT is then employed to extract contextual semantics for fine-tuning, leading to the generation of weighted embeddings and difference matrices. Ultimately, entity resolution is achieved through a single-layer CNN and HighwayNet.

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