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生物表达语言中知识图谱的再策展和合理丰富化。

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language.

机构信息

Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.

Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.

出版信息

Database (Oxford). 2019 Jan 1;2019. doi: 10.1093/database/baz068.

Abstract

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.

摘要

新的生物医学文献的快速积累不仅导致已编目知识图谱 (KG) 过时和不完整,而且使得人工编目成为不切实际和不可持续的解决方案。有必要采用自动化或半自动化工作流程来协助对文献进行优先级排序和编目,以更新和丰富 KGs。我们开发了两种工作流程:一种用于重新编目给定的 KG,以确保其语法和语义质量,另一种用于通过手动修改信息密度低的节点自动提取关系来合理地丰富它。我们使用从 MEDLINE 摘要和 PubMed Central 全文文章中提取的内容,应用这些工作流程对来自 NeuroMMSig 数据库的 Biological Expression Language 中编码的 KGs 进行处理,这些内容是使用文本挖掘输出通过 INDRA 集成的。我们已经在 https://github.com/bel-enrichment/bel-enrichment 上免费提供了此工作流程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57bf/6587072/ff80ccf1c556/baz068f1.jpg

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