Cluster-Level Evidence-Enhanced Truth Inference for Knowledge Graph Fusion

Factual conflicts after entity alignment in knowledge graph fusion (KGF) can affect the consistency and reliability of fusion results. To address the difficulty of directly transferring existing traditional truth inference and fact verification methods to knowledge graph fusion, a cluster-level evidence-enhanced truth-inference method for knowledge graph fusion (CETI-KGF) is proposed. The method adopts a two-stage fact modeling strategy to organize semantically related multi-source facts and perform truth inference at the cluster level. Semantic-aware fact clustering (SFC) is constructed to divide facts into different fact groups. A cluster-level credibility evidence modeling (CEM) module is then employed to construct cluster-level evidence based on source support, count-based support, and local consistency within the knowledge graph. Finally, structured prompts guide the large language model (LLM) to jointly infer truths in a mixed single-truth and multi-truth setting, and ultimately map the resulting decisions to the facts within each cluster. The FR-EN subset of the entity alignment (EA) dataset DBP15K is used as the experimental setting, and a dataset containing 5429 conflict groups and 17,067 candidate facts is constructed for experimentation. The proposed method achieves the highest Error Precision and Error F1 among the compared methods, reaching 81.0% and 75.7%, respectively. Compared with directly using an LLM, the proposed method improves Error Precision and Error F1 by 10.5 and 6.0 percentage points, respectively.