OWL2VecOA OMIM-ORDO Alignment Dataset with AML and LogMap Integration

The repository contains results of applying the OWL2VecOA method to biomedical ontology alignments, specifically focusing on the alignment between OMIM and ORDO. The specifications are: walk depth = 3, embedding size =100, iteration =70, walker iteration k=20. The alignment process utilized a combined approach, integrating results from two well-established ontology matching systems: AML and LogMap. Specifically the following input configurations were used: Train.tsv (from BIO-ML Track 2023) combined with the intersection of AML and LogMap alignments Train.tsv combined with the union of AML and LogMap alignments Train.tsv combined with LogMap alignments (Logmapping) Train.tsv combined with LogMap alignments (Anchor Mappings) Train.tsv combined with LogMap alignments (OverEstimation Mappings) Train.tsv only The results package contains three key components of each input data: Embedding file, Cosine Similarity Scores file and Euclidian Distance Scores file. The embedding files can be used for various ML downstream tasks, while the similarity and distance scores provide direct measures of entity relatedness, potentially useful for ontology alignment, entity matching, or other biomedical informatics applications.本仓库收录了将OWL2VecOA方法应用于生物医学本体对齐任务的实验结果，研究聚焦于OMIM (Online Mendelian Inheritance in Man)与ORDO (Orphanet Rare Disease Ontology)之间的本体对齐。 本次实验的参数配置如下：游走深度=3，嵌入维度=100，迭代次数=70，游走器迭代次数k=20。 本次对齐流程采用融合式方案，整合了两款成熟的本体匹配系统AML与LogMap的对齐结果，具体使用的输入配置如下： 1. 结合2023年BIO-ML赛道Train.tsv数据集与AML、LogMap对齐结果的交集 2. 结合2023年BIO-ML赛道Train.tsv数据集与AML、LogMap对齐结果的并集 3. 结合LogMap对齐结果（Logmapping）的Train.tsv数据集 4. 结合LogMap对齐结果（锚定映射(Anchor Mappings)）的Train.tsv数据集 5. 结合LogMap对齐结果（高估映射(OverEstimation Mappings)）的Train.tsv数据集 6. 仅使用Train.tsv数据集 本次实验的结果包涵盖了每个输入配置对应的三类核心文件：嵌入文件、余弦相似度得分文件与欧氏距离得分文件。其中，嵌入文件可用于各类机器学习下游任务；相似度与距离得分可直接表征实体间的关联程度，可应用于本体对齐、实体匹配或其他生物医学信息学相关场景。