Building a biomedical ontology recommender web service.
Building a biomedical ontology recommender web service.
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DOI：
10.1186/2041-1480-1-s1-s1
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发表时间：
2010-06-22
影响因子：
1.9
通讯作者：
Shah NH
中科院分区：
文献类型：
--
作者：
Jonquet C;Musen MA;Shah NH
生物医学信息学的研究人员使用本体和术语来注释他们的数据，以促进数据集成和翻译发现。随着本体用于生物医学数据集注释的使用的增加，一个共同的挑战是识别最适合于注释特定数据集的本体。生物医学本体的数量和种类是巨大的，并且对于研究人员来说找出使用哪个本体是麻烦的。我们提出了生物医学本体推荐网络服务。该系统使用文本元数据或描述感兴趣的领域的一组关键字，并建议用于注释或表示数据的适当本体。该服务基于三个标准做出决定。第一个是覆盖率，或提供覆盖输入文本的大多数术语的本体。第二个是连通性，或者说是最常被其他本体映射到的本体。最后一个标准是大小，或者说本体中概念的数量。该服务根据使用国家生物医学本体中心（NCBO）注释器网络服务创建的注释的分数来对本体进行评分。我们使用了UMLS Metathesaurus和NCBO BioPortal中的所有本体。我们比较和对比我们的推荐通过详尽的功能比较，以前发表的努力。我们在三个真实的用例的背景下评估和讨论了几个推荐算法的结果。最好的推荐策略，被专家评估者评为“非常相关”，是基于覆盖范围和连接标准的。推荐服务（alpha版本）可供社区使用，并嵌入BioPortal。
Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use. We present the Biomedical Ontology Recommender web service. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is coverage, or the ontologies that provide most terms covering the input text. The second is connectivity, or the ontologies that are most often mapped to by other ontologies. The final criterion is size, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) Annotator web service. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal. We compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated ‘very relevant’ by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.