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基于上下文的进化生命科学本体论中映射的细化。

Context-based refinement of mappings in evolving life science ontologies.

机构信息

Institute of Computing, University of Campinas, Campinas, SP, Brazil.

出版信息

J Biomed Semantics. 2023 Oct 19;14(1):16. doi: 10.1186/s13326-023-00294-8.

Abstract

BACKGROUND

Biomedical computational systems benefit from ontologies and their associated mappings. Indeed, aligned ontologies in life sciences play a central role in several semantic-enabled tasks, especially in data exchange. It is crucial to maintain up-to-date alignments according to new knowledge inserted in novel ontology releases. Refining ontology mappings in place, based on adding concepts, demands further research.

RESULTS

This article studies the mapping refinement phenomenon by proposing techniques to refine a set of established mappings based on the evolution of biomedical ontologies. In our first analysis, we investigate ways of suggesting correspondences with the new ontology version without applying a matching operation to the whole set of ontology entities. In the second analysis, the refinement technique enables deriving new mappings and updating the semantic type of the mapping beyond equivalence. Our study explores the neighborhood of concepts in the alignment process to refine mapping sets.

CONCLUSION

Experimental evaluations with several versions of aligned biomedical ontologies were conducted. Those experiments demonstrated the usefulness of ontology evolution changes to support the process of mapping refinement. Furthermore, using context in ontological concepts was effective in our techniques.

摘要

背景

生物医学计算系统受益于本体及其相关映射。实际上,生命科学中的对齐本体在多个语义启用任务中起着核心作用,尤其是在数据交换中。根据新的本体版本中插入的新知识,保持最新的对齐至关重要。基于添加概念,原地细化本体映射需要进一步研究。

结果

本文通过提出基于生物医学本体演变细化一组现有映射的技术来研究映射细化现象。在我们的第一个分析中,我们研究了在不将匹配操作应用于整个本体实体集的情况下,如何使用新的本体版本建议对应关系的方法。在第二个分析中,细化技术可以派生新的映射,并更新映射的语义类型,超越等价。我们的研究探索了对齐过程中概念的邻域,以细化映射集。

结论

对几个对齐的生物医学本体版本进行了实验评估。这些实验表明,本体演变变化可用于支持映射细化过程。此外,在我们的技术中使用本体概念的上下文是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b268/10585791/b2c2071eb41f/13326_2023_294_Fig1_HTML.jpg

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