Multi-rule Graph Construction Network for Entity Linking in Visually Rich Documents

Entity linking in visually rich documents (VRDs) is critical for industrial automation but faces challenges from complex layouts and computational inefficiency in existing models. Traditional approaches relying on pre-trained transformers or graph networks struggle with noisy OCR outputs, a high number of parameters, and invalid edge predictions in industrial VRDs. To Vision Information Extraction (VIE) for Industrial VRDs, we introduce a lightweight multi-rule graph construction network for entity linking, integrating text and layout embeddings as graph nodes. A multi-rule filtering method reduces invalid edges using node distance, link interference, and standardization rules inspired by document production logic and reading habit. A node relation enhancement module with Graph Attention Networks (GAT) enhances nodes through multi-rule edges and attention scores, enabling robust reasoning for noisy and complex layouts. Evaluated on FUNSD and SIBR datasets, our model achieves F1 scores of 58.96% and 65.08% with only 18M parameters, outperforming non-pretrained baselines while maintaining deployability for resource-constrained environments.