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从可视化视角分析用户与生物医学本体的交互

Analyzing user interactions with biomedical ontologies: A visual perspective.

作者信息

Kamdar Maulik R, Walk Simon, Tudorache Tania, Musen Mark A

机构信息

Stanford Center for Biomedical Informatics Research, Stanford University, USA.

出版信息

Web Semant. 2018 Mar;49:16-30. doi: 10.1016/j.websem.2017.12.002. Epub 2017 Dec 20.

Abstract

Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal.

摘要

生物医学本体规模庞大

生物门户知识库中的多个本体包含数千甚至数十万实体。开发和维护如此大规模的本体很困难。为了在工作中支持本体作者和知识库开发者,关键是要加深我们对生物医学研究人员如何在下游应用中探索、查询、重用和使用这些本体的理解。我们通过分析多年来不同访问模式下的生物门户交互日志,对生物门户本体知识库中的用户活动进行了探索性实证分析。我们研究生物门户的用户如何查询和搜索本体及其类,他们如何探索本体,以及他们如何重用不同本体中的类。此外,通过三个实际场景,我们不仅分析了本体在注释任务中的使用情况,还将其与生物门户用户的浏览和查询行为进行了比较。在我们的调查中,我们使用了几种不同的可视化技术。为了一眼就能检查大量的交互、重用和实际使用数据,我们利用并扩展了PolygOnto,这是一种在之前的工作中已成功用于分析本体重用的可视化方法。我们的结果表明,探索、查询、重用和实际使用行为很少一致,这表明不同用户倾向于探索、查询和使用本体的不同部分。最后,我们强调并讨论了生物门户用户之间的差异和共性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/5895104/b799786055f6/nihms930894f1.jpg

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本文引用的文献

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BiOnIC: A Catalog of User Interactions with Biomedical Ontologies.BiOnIC:生物医学本体用户交互目录。
Semant Web ISWC. 2017 Oct;10588:130-138. doi: 10.1007/978-3-319-68204-4_13. Epub 2017 Oct 4.
6
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations.NHGRI GWAS Catalog,一个经过精心策划的 SNP 与特征关联资源。
Nucleic Acids Res. 2014 Jan;42(Database issue):D1001-6. doi: 10.1093/nar/gkt1229. Epub 2013 Dec 6.
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Predicting the extension of biomedical ontologies.预测生物医学本体的扩展。
PLoS Comput Biol. 2012;8(9):e1002630. doi: 10.1371/journal.pcbi.1002630. Epub 2012 Sep 13.

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