A Multi-Information Fusion Unsupervised Entity Alignment Model for Knowledge Graphs in Oil and Gas Pipeline Safety

Targeting the joint challenges posed by sparse graph topology, limited semantic expressiveness, and scarce annotation resources that commonly afflict knowledge graphs in the oil and gas pipeline safety domain, this paper presents a Multi-Information Fusion Unsupervised Entity Alignment model (MIF-UEA). The proposed method constructs high-quality initial alignment pairs by integrating multi-source similarity computation with a structure-aware seed generation mechanism and performs representation learning by fusing structural features and semantic attribute information. Furthermore, a pseudo-label augmentation and denoising strategy is introduced to enhance the effectiveness of self-training. Finally, entity matching is achieved through an optimal transport model. Experimental results confirm that MIF-UEA surpasses existing baselines across both the specialized oil and gas pipeline safety dataset and multiple general-domain benchmarks, demonstrating its effectiveness and generalization capability.