Biomedical ontology offers a structured framework to model the biomedical knowledge in a machine-readable format. However, the heterogeneity inherent in biomedical ontologies hinders their communication. Biomedical Ontology Matching (BOM) can address this issue by identifying equivalent concepts in biomedical ontologies. Recently, Evolutionary Algorithms (EAs) based matching techniques have exhibited their effectiveness in finding high-quality matching results. However, due to the vast number of entities, and intricate relationships between entities, it is difficult for traditional EAs to efficiently solve the BOM problem. To tackle this challenge, this paper proposes an efficient BOM method to automatically match large-scale biomedical ontologies. First, a novel anchor-based biomedical ontology partitioning method is developed to transform the large-scale BOM problem into several small-scale matching tasks, reducing the search space of the matching phase. Second, a new Compact Geometric Semantic Genetic Programming (CGSGP) is proposed to efficiently construct high-level Similarity Feature for BOM, which can significantly reduce the computational complexity. Lastly, a new fitness function composed of the approximated evaluation metric and the Dominance Improvement Ratio (DIR) is introduced, which can overcome the solution’s bias improvement and enable the simultaneous matching of multiple pairs of sub-ontologies without requiring the standard alignment. The experiment verifies our approach’s performance on the Ontology Alignment Evaluation Initiative (OAEI)’s Anatomy, Large Biomed and Disease and Phenotype datasets. The experimental results show that our method can efficiently determine high-quality BOM results across different test cases, whose performance significantly outperforms the state-of-the-art BOM techniques.

中文翻译：

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基于锚点的生物医学本体划分和紧凑几何语义遗传编程的高效大规模生物医学本体匹配


生物医学本体提供了一个结构化框架，以机器可读的格式对生物医学知识进行建模。然而，生物医学本体固有的异质性阻碍了它们的交流。生物医学本体匹配（BOM）可以通过识别生物医学本体中的等效概念来解决这个问题。最近，基于进化算法（EA）的匹配技术已经展示了其在寻找高质量匹配结果方面的有效性。然而，由于实体数量庞大，实体之间的关系错综复杂，传统EA很难有效解决BOM问题。为了应对这一挑战，本文提出了一种高效的 BOM 方法来自动匹配大规模生物医学本体。首先，开发了一种新颖的基于锚的生物医学本体划分方法，将大规模BOM问题转化为多个小规模匹配任务，减少了匹配阶段的搜索空间。其次，提出了一种新的紧凑几何语义遗传编程（CGSGP）来有效地构建BOM的高级相似性特征，这可以显着降低计算复杂度。最后，引入了由近似评估指标和优势改进比（DIR）组成的新适应度函数，该函数可以克服解决方案的偏差改进，并且无需标准对齐即可实现多对子本体的同时匹配。该实验验证了我们的方法在本体对齐评估计划 (OAEI) 的解剖学、大型生物医学以及疾病和表型数据集上的性能。 实验结果表明，我们的方法可以在不同的测试用例中有效地确定高质量的 BOM 结果，其性能显着优于最先进的 BOM 技术。