Faulty Real-World Knowledge Bases

该数据集是一个包含20个现实世界中不一致或连贯性较差的本体论的基准，用于在顺序诊断场景中比较DynamicHS和HS-Tree算法的性能。该数据集中的部分内容已在多项研究中被探讨。在此数据集中，以大写字母开头的本体论错误是由人为建模错误造成的，而以小写字母开头的错误则源于自动合并了由人类建模的本体论。该数据集的规模为20个本体论，其任务是进行知识库的顺序诊断。

This dataset is a benchmark containing 20 real-world ontologies that are inconsistent or have poor coherence, designed to compare the performance of DynamicHS and HS-Tree algorithms in sequential diagnosis scenarios. Portions of this dataset have been explored in multiple studies. In this dataset, ontology errors starting with uppercase letters are caused by human modeling errors, while those starting with lowercase letters stem from the automatic merging of human-modeled ontologies. This dataset comprises 20 ontologies, and its task is to perform sequential diagnosis of knowledge bases.