CSpace
Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision
Li, Weizhuo1,2; Zhang, Songmao3
2020-12-17
发表期刊KNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
卷号209页码:21
摘要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 (R) 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. (C) 2020 Elsevier B.V. All rights reserved.
关键词Mapping validation and repair Biomedical ontologies Probabilistic description logics Belief revision
DOI10.1016/j.knosys.2020.106436
收录类别SCI
语种英语
资助项目National Key Research and Development Program of China[2016YFB1000902] ; Natural Science Foundation of China[U1736204] ; Natural Science Foundation of China[62006125] ; Natural Science Foundation of China[61621003] ; Natural Science Foundation of China[61906037]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000582518100010
出版者ELSEVIER
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/52383
专题中国科学院数学与系统科学研究院
通讯作者Li, Weizhuo
作者单位1.Nanjing Univ Posts & Telecommun, Sch Modern Posts & Inst Modern Posts, Nanjing, Peoples R China
2.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
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Li, Weizhuo,Zhang, Songmao. Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision[J]. KNOWLEDGE-BASED SYSTEMS,2020,209:21.
APA Li, Weizhuo,&Zhang, Songmao.(2020).Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision.KNOWLEDGE-BASED SYSTEMS,209,21.
MLA Li, Weizhuo,et al."Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision".KNOWLEDGE-BASED SYSTEMS 209(2020):21.
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