Multi-Chain Fusion Reasoning for Knowledge Graph Link Prediction

The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain fusion reasoning framework to realize accurate link prediction. First, a dual retrieval mechanism based on semantic similarity metrics and embedded feature matching is designed to construct a high-confidence candidate entity set; second, entity-attribute chains, entity-relationship chains, and historical context chains are established by integrating context information from external knowledge bases to generate a candidate entity set. Finally, a self-consistency scoring method fusing type constraints and semantic space alignment is proposed to realize the joint validation of structural rationality and semantic relevance of candidate entities. Experiments on two public datasets show that the method in this paper fully utilizes the ability of multi-chain reasoning and significantly improves the accuracy of knowledge graph link prediction.