关键词: 知识图谱, 实体对齐, 主题先验, 路径增强双上下文, 课程式对比学习

Abstract: Entity alignment(EA) in multi-source heterogeneous knowledge graphs(KG) is crucial for identifying semantically equivalent entities across graphs. To address the difficulty of separating hard negative samples—entities that are highly similar in semantics but not equivalent—and the matching errors caused by relying only on local structure or textual similarity, a Topic-Prior-Guided Dual-Context Entity Alignment (TPDC) model was proposed. A topic model was built from entity attribute texts to derive entity-level topic distributions, which served as topic priors to guide candidate pool construction and difficulty stratification, thus constraining the search space to a semantically concentrated subspace. A dual-context encoding network with neighbor and relation branches was designed to capture fine-grained structural semantics from multi-hop neighbors and relation paths. A curriculum contrastive learning strategy was introduced to increase the sampling ratio and loss weights of hard negatives in an easy-to-hard manner, improving discrimination in late training. Results on DBP15K show that Hits at 1 (Hits@1) increases by 0.04 and 5.18 percentage points over the second-best baseline on two subsets, Hits at 10 (Hits@10) increases by 0.32,1.13, and 0.05 percentage points, and Mean Reciprocal Rank (MRR) increases by 0.026,0.106,and 0.053,confirming better overall ranking quality and robust handling of hard negatives.

Key words: Knowledge Graphs(KG), Entity Alignment(EA), Topic Prior, Path-Enhanced Dual-Context, Curriculum Contrastive Learning