The ontology matching approaches identify correspondences among entities across ontologies, and the quality of ontology mappings is crucial for supporting knowledge sharing and reuse on the Semantic Web. In the annual Ontology Evaluation Alignment Initiative (OAEI) competitions, matching large and complex, real-world biomedical ontologies is one of the most challenging endeavors. As matching methods are basically heuristic, wrong mappings often exist in the generated alignments. The general framework of mapping validation collects candidate wrong mappings based on unsatisfiable concepts and adopts the removal strategy to gain the coherence of alignments w.r.t. source ontologies. Although it ensures logical coherence, such repairing does not necessarily guarantee the quality of mappings obtained, i.e., the disposed mappings can be positive and the retained ones can be wrong in terms of the domain knowledge intended in the ontologies per se. This can be demonstrated by the existence of incoherences when the UMLS Metathesaurus® has been used as the basis of reference alignments for OAEI biomedical ontology matching tasks. To address this problem, we propose a novel approach for repairing biomedical ontology mappings by probabilistic reasoning and belief revision techniques, featuring a combination of removal strategy and revision strategy. More concretely, mappings are transformed into probabilistic description logics (PDL) conditional constraints and their weights into probability intervals based on our designed rules. Then, the incoherence checking of mappings is reduced to solving a linear program with the constraints under the PDL semantics. For identified incoherent mappings, instead of simply discarding, we revise them by relaxing their probability intervals until the probabilistic coherence is reached. The evaluation on repairing OAEI biomedical alignments shows that our approach can be effective in retaining correct mappings and removing wrong ones. Moreover, we show that repair systems following the general framework of mapping validation have improved their performance when equipped with our revision module at the repair stage. Furthermore, feeding structural matchers with repaired alignments as seeds shows that the mappings generated by our approach lead to the best structural matching result compared with other repair systems. Being non-aggressive, our approach is suitable for applications like ontology-supported medical information retrieval, semantic annotation and indexing of medical articles, and matchmaking and ranking objects among multiple ontologies.

本体匹配方法识别跨本体的实体之间的对应关系，并且本体映射的质量对于支持语义Web上的知识共享和重用至关重要。在一年一度的本体论评估联盟计划（OAEI）竞赛中，匹配大型和复杂的，现实世界中的生物医学本体论是最具挑战性的工作之一。由于匹配方法基本上是启发式的，因此在生成的比对中通常存在错误的映射。映射验证的通用框架基于不满意的概念收集候选错误映射，并采用删除策略来获得源本体的比对一致性。尽管它可以确保逻辑上的连贯性，但这种修复并不一定保证所获得映射的质量，即本身。当将UMLSMetathesaurus®用作OAEI生物医学本体匹配任务参考比对的基础时，可以证明存在不一致性。为了解决这个问题，我们提出了一种通过概率推理和信念修订技术修复生物医学本体映射的新方法，该方法结合了删除策略和修订策略。更具体地说，根据我们设计的规则，映射被转换为概率描述逻辑（PDL）条件约束，其权重被转换为概率区间。然后，减少了映射的不一致性检查，以解决具有PDL语义约束的线性程序。对于已识别的不相干映射，而不是简单地丢弃，我们通过放宽它们的概率间隔来修改它们，直到达到概率一致性为止。对修复OAEI生物医学比对的评估表明，我们的方法可以有效地保留正确的图谱并消除错误的图谱。此外，我们证明，在修复阶段配备我们的修订模块时，遵循通用映射验证框架的修复系统已改善了其性能。此外，将具有已修复路线的结构匹配器作为种子供入表明，与其他修复系统相比，我们的方法生成的映射可导致最佳结构匹配结果。由于方法不具攻击性，因此适用于诸如本体支持的医学信息检索，医学文章的语义标注和索引，