BCSP: Collaborative Research: ABI Development: Exploring Taxon Concepts (ETC) through analyzing fine-grained semantic markup of descriptive literature

BCSP：协作研究：ABI 开发：通过分析描述性文献的细粒度语义标记探索分类概念 (ETC)

• 批准号: 1147266
• 负责人: Hong Cui
• 金额: $ 109.57万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147266&HistoricalAwards=false
• 关键词: BCSP; Collaborative; Research; ABI; Development

A collaborative award has been made to the University of Arizona and the University of California at Davis to develop novel ways of tying scientific names directly to published biological characteristics of organisms, and to implement a new user-friendly program, the Explorer of Taxon Concepts (ETC), to assist with the disambiguation of the scientific names of species at all taxonomic ranks. Prototypes from several successful NSF-funded projects are integrated through ETC to enable: (i) text-mining extraction of taxonomic knowledge from scientific literature, (ii) analysis and integration of this knowledge using logic-based reasoning and information theoretic methods, and (iii) result visualization. The results shed light on similarities and differences among various scientists' understanding of a particular species, as well as relations between the terminology used by different scientists, allowing for more accurate integration of data gathered by different investigators. A component of the ETC project is computer science research aimed at a novel integration of state-of-the-art logic inference and information theoretic approaches to taxonomic science.Scientific names are the primary identifiers for organisms and the anchor for the communication and comparison of biological knowledge. However, there is constant revision of the definition of taxa by experts, making interpretation of the names through time challenging. This project will produce and demonstrate the use of ETC software on descriptive scientific literature from the Rosaceae (the Rose family) and Apoidea (the Bee super-family) to facilitate research into critical pollination systems. These pollination systems are currently of great concern due to reductions in bee populations globally with the potential to reduce yield of many staple food crops. ETC's components support scientific knowledge value added to its inputs, making them useful in many other biodiversity information applications. Character and anatomy ontologies built and enhanced by the ETC project will benefit all knowledge-based applications in biology. The project adopts the following strategies to broaden its accessibility: The integration of ETC components with existing biological computing infrastructure such as DataONE and iPlant will make the tools broadly available. The partnership with iPlant's successful Education, Outreach, and Training (EOT) group will document the software for instructional use and encourage its adoption in the classroom. Components of the research and the final products will also be packaged into learning modules for college and graduate level courses at University of Arizona, the University of California at Davis, and other universities. Project outcomes will be accessible via the link provided at: http://sirls.arizona.edu/node/684.

亚利桑那大学（University of Arizona）和加州大学戴维斯分校（University of California at Davis）获得了一项合作奖，以开发将学名直接与已发表的生物体生物学特征联系起来的新方法，并实施一个新的用户友好程序，即分类单元概念探索者（Explorer of Taxon Concepts， ETC），以协助消除所有分类级别物种的学名歧义。来自几个成功的nsf资助项目的原型通过ETC集成，以实现：(i)从科学文献中提取分类知识的文本挖掘，（ii）使用基于逻辑的推理和信息理论方法对这些知识进行分析和集成，以及（iii）结果可视化。研究结果揭示了不同科学家对特定物种的理解的异同，以及不同科学家使用的术语之间的关系，从而可以更准确地整合不同研究人员收集的数据。ETC项目的一个组成部分是计算机科学研究，旨在将最先进的逻辑推理和信息理论方法整合到分类学科学中。学名是生物的主要标识，是生物知识交流和比较的基础。然而，专家们对分类群的定义不断修订，使得对这些名称的解释随着时间的推移变得具有挑战性。该项目将制作和演示ETC软件对蔷薇科（玫瑰科）和蜜蜂科（蜜蜂超级科）的描述性科学文献的使用，以促进对关键授粉系统的研究。由于全球蜜蜂种群数量的减少，这些授粉系统目前受到极大关注，有可能降低许多主粮作物的产量。ETC的组成部分支持在其投入中增加科学知识价值，使它们在许多其他生物多样性信息应用中有用。ETC项目建立和增强的特征和解剖本体将有利于生物学中所有基于知识的应用。该项目采用以下策略来扩大其可访问性：ETC组件与现有生物计算基础设施（如DataONE和iPlant）的集成将使这些工具广泛可用。与iPlant成功的教育、推广和培训（EOT）小组的合作将记录教学使用的软件，并鼓励其在课堂上的采用。研究的组成部分和最终产品也将被打包成亚利桑那大学、加州大学戴维斯分校和其他大学的大学和研究生水平课程的学习模块。项目成果可通过以下链接获取：http://sirls.arizona.edu/node/684。