神经学者系统中的知识获取工具及其在解剖束追踪数据中的应用。

Gully A P C Burns, Wei-Cheng Cheng
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引用次数: 52

摘要

背景:知识库总结了已发表的文献,为系统级生物学的特定领域提供了有用的在线参考,而这些领域没有大型数据库的支持。在神经解剖学领域,一些专注的小团队已经建立了中等规模的知识库,以总结描述几种物种的牵道追踪实验的文献。尽管经过多年的整理和管理,这些数据库只提供了部分可用的已发表文献。考虑到阅读这些论文的科学家都必须生成通常会被输入到这样一个系统中的解释,我们在这里尝试提供通用的注释工具,使社区成员能够轻松地参与数据整理任务。结果:在本文中,我们描述了一个名为“神经学者”的开源,免费提供的知识管理系统,该系统允许根据精心设计的模式对PDF文件进行直接结构化标记,以捕获该类实验的基本细节。尽管本文中所使用的示例是针对神经解剖学连接的,但该设计是可自由扩展的,并且可以想象用于构建其他实验类型的局部知识库。实验的知识表示也直接与原始研究文章的贡献文本片段相关联。通过使用该系统,不仅社区成员可以参与整理任务,而且可以收集输入数据,以便通过使用自然语言处理(NLP)自动获取知识。结论:我们提出了一个功能性的工作工具,允许用户通过使用结构化问卷从文献中填充神经解剖学连接数据的知识库。该系统是开源的,功能齐全,可从[1]下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Tools for knowledge acquisition within the NeuroScholar system and their application to anatomical tract-tracing data.

Background: Knowledge bases that summarize the published literature provide useful online references for specific areas of systems-level biology that are not otherwise supported by large-scale databases. In the field of neuroanatomy, groups of small focused teams have constructed medium size knowledge bases to summarize the literature describing tract-tracing experiments in several species. Despite years of collation and curation, these databases only provide partial coverage of the available published literature. Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation.

Results: In this paper, we describe an open-source, freely available knowledge management system called 'NeuroScholar' that allows straightforward structured markup of the PDF files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. Through the use of this system, not only could members of the community contribute to the collation task, but input data can be gathered for automated approaches to permit knowledge acquisition through the use of Natural Language Processing (NLP).

Conclusion: We present a functional, working tool to permit users to populate knowledge bases for neuroanatomical connectivity data from the literature through the use of structured questionnaires. This system is open-source, fully functional and available for download from [1].

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