Garcia Alexander, Lopez Federico, Garcia Leyla, Giraldo Olga, Bucheli Victor, Dumontier Michel
Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain.
Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Colombia.
PeerJ. 2018 Jan 2;6:e4201. doi: 10.7717/peerj.4201. eCollection 2018.
A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.
很大一部分生物医学文献的呈现方式使得用户难以通过计算查询来查找或汇总内容。促进科学文献再利用的一种方法是使用标准化的网络技术将这些信息构建为关联数据。在本文中，我们展示了Biotea的第二个版本，这是一个语义化的、关联数据版本的美国国立医学图书馆生物医学文献数据库（PubMed Central）开放获取子集，通过使用美国国立生物医学本体中心的现有基础设施的专门注释管道进行了增强。我们公开了我们的模型、服务、软件和数据集。我们的基础设施支持手动和半自动注释，生成的数据表示为基于资源描述框架（RDF）的关联数据，并可以使用SPARQL查询语言轻松查询。我们通过几个用例说明了我们系统的实用性。我们的数据集、方法和技术可在http://biotea.github.io上获取。
