A causal discovery-based adaptive fusion algorithm for multi-source heterogeneous knowledge graphs

Multi-source heterogeneous knowledge graph fusion faces significant challenges due to schema heterogeneity, entity conflicts, and relationship inconsistencies across different knowledge sources. This paper proposes CausalFusion, a novel adaptive fusion algorithm that leverages causal discovery principles to guide the knowledge graph integration process. The algorithm incorporates a constraint-based causal discovery component specifically designed for relational data, an adaptive weight learning mechanism that dynamically adjusts source contributions based on causal strength, and a conflict resolution strategy that prioritizes causal consistency over statistical correlation. Experimental evaluation on benchmark datasets including DBpedia, Freebase, YAGO, and Wikidata demonstrates significant improvements in fusion quality, with the proposed method achieving 91.2% precision and 88.7% recall, outperforming state-of-the-art baselines by 1.9% and 1.5% respectively. The results validate the effectiveness of incorporating causal inference into knowledge graph fusion, particularly for preserving meaningful causal relationships while resolving heterogeneity conflicts.