Accurate biomedical Knowledge Graph (KG) alignment is critical for integrating heterogeneous biomedical information and enabling reliable downstream applications, such as clinical decision support, biomedical search, and personalized healthcare. However, variations in terminologies, KG structures, and semantic granularity across biomedical sources introduce substantial heterogeneity, making the construction of high-quality Similarity Features (SFs) a major challenge. Although the Generative Pre-trained Transformer (GPT) has shown strong potential in capturing nuanced biomedical semantics, each GPT-augmented SF typically reflects only one semantic perspective, and combining multiple SFs in a meaningful manner remains non-trivial because of the expanded symbolic search space and potential feature conflicts. To address these challenges, we propose a novel Tree-based Particle Swarm Optimization with Adaptive Fitness Optimization (T-PSO-AFO) framework for GPT-augmented SF construction. First, a GPT-based semantic feature construction method is introduced to extract expressive and context-aware SFs that better capture biomedical entity equivalence. Then, a tree-based symbolic representation within a PSO framework is developed to explore diverse and complex SF combinations more effectively. Finally, an adaptive fitness landscape optimization mechanism is proposed to dynamically reshape the symbolic search space during evolution, improving the convergence stability and alignment performance. Experiments on OAEI's LargeBio and Disease and Phenotype datasets demonstrate that T-PSO-AFO significantly outperforms state-of-the-art biomedical entity matching approaches, validating its robustness, effectiveness, and scalability in aligning heterogeneous biomedical KGs.
Xue et al. (Thu,) studied this question.