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发布关于医学主题词(MeSH)在医学在线数据库(MEDLINE)中共现情况的生物医学预测知识库。

Publishing Biomedical Predication Repository About MeSH Co-Occurrences in MEDLINE.

作者信息

Miñarro-Giménez Jose Antonio, Martínez Manuel Quesada, Fernández-Breis Jesualdo Tomás, Schulz Stefan

机构信息

Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.

Facultad de Informática, University of Murcia, IMIB-Arrixaca, Spain.

出版信息

Stud Health Technol Inform. 2016;228:765-9.

Abstract

The construction and publication of predications form scientific literature databases like MEDLINE is necessary due to the large amount of resources available. The main goal is to infer meaningful predicates between relevant co-occurring MeSH concepts manually annotated from MEDLINE records. The resulting predications are formed as subject-predicate-object triples. We exploit the content of MRCOC file to extract the MeSH indexing terms (main headings and subheadings) of MEDLINE. The predications were inferred by combining the semantic predicates from SemMedDB, the clustering of MeSH terms by their associated MeSH subheadings and the frequency of relevant terms in the abstracts of MEDLINE records. The inferring process also obtains and associates a weight to each generated predication. As a result, we published the generated dataset of predications using the Linked Data principles to make it available for future projects.

摘要

由于有大量可用资源,从诸如MEDLINE这样的科学文献数据库构建和发布预测是必要的。主要目标是在从MEDLINE记录中手动注释的相关共现MeSH概念之间推断有意义的谓词。生成的预测形为主语-谓语-宾语三元组。我们利用MRCOC文件的内容来提取MEDLINE的MeSH索引词(主要标题和副标题)。通过结合来自SemMedDB的语义谓词、根据相关MeSH副标题对MeSH词进行的聚类以及MEDLINE记录摘要中相关词的频率来推断预测。推断过程还为每个生成的预测获取并关联一个权重。结果,我们使用关联数据原则发布了生成的预测数据集,以供未来项目使用。

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