EAGER: Collaborative Research: Mining Scientific Literature with the LAPPS Grid

科学家们已经无法跟上科学出版物数量的不断增加。缺乏这种能力是科学进步的根本瓶颈。目前的搜索技术是有限的，因为他们能够找到许多相关的文件，但不能提取和组织这些文件的信息内容或建议新的科学假设的基础上组织的内容。基于自然语言处理（NLP）的文本挖掘策略是解决这个问题的公认方法，但大多数科学家没有专业知识或时间来使用它们。此外，NLP工具之间缺乏互操作性以及分散在网络上的存储库中的数据是共享工作流程，资源和结果的障碍。该项目将确定在一个易于使用的科学文本挖掘平台中需要哪些分析功能，实现这样一个平台的初始版本，并将其提供给科学家。目前还没有一个开放的、易于使用的科学文本挖掘平台，可以提供对广泛的软件、计算资源和出版数据的互操作访问。公开可用的软件（如Google）并不适合发布数据，内部工具也很脆弱，只能提供一小部分相关结果。因此，该项目的主要目标是：（1）确定科学出版物中采矿信息的易用平台的要求;（2）部署满足这些需要的设施。为了实现这一目标，该项目将扩展现有的NSF资助的LAPPS网格，以包括访问广泛的可互操作的NLP工具，大量出版数据和词汇和本体资源的方法，并且，至关重要的是，快速调整现有软件以适应新的领域并评估结果。该项目还将利用NSF资助的Galaxy平台的增强功能，以进行交互式数据探索和扩展对NSF硬件资源（XSEDE机器，包括Stampede，Bridges和Jetstream）的访问。通过提供获取采矿科学出版物服务的途径，并降低因许可证、再分配和知识产权问题而造成的进入壁垒，该项目提供了科学家以前无法获得的能力。研究人员能够通过基于Web的界面使用HPC基础设施执行大规模文本挖掘，而无需了解底层基础设施。此外，提供迭代域适配功能使科学家能够轻松地将现有服务适配到特定领域，而无需配置或安装其他组件。对分散在大量出版物存储库中的显性和隐性信息进行检查的能力无疑将产生新的观察和见解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Scientists have become unable to keep up with the ever-expanding number of scientific publications. The lack of this ability is a fundamental bottleneck to scientific progress. Current search technologies are limited because they are able to find many relevant documents, but cannot extract and organize the information content of these documents or suggest new scientific hypotheses based on the organized content. Natural Language Processing (NLP) based text mining strategies are a recognized means to approach this problem, but most scientists do not have the expertise or time to take use them. In addition, the lack of interoperability among NLP tools as well as the data in repositories scattered around the web are barriers to sharing workflows, resources, and results. This project will identify what analysis features are needed within an easy-to-use platform for mining scientific texts, implement an initial version of such a platform, and make it available to scientists.There is currently no open, easy-to-use platform for mining scientific texts that provides interoperable access to a wide array of software, computing resources, and publication data. Publicly available software (such as Google) is not geared toward publication data, and in-house tools are fragile and deliver only a fraction of relevant results. The main objective of this project is, therefore, to (1) identify the requirements for an easy-to-use platform for mining information from scientific publications and (2) deploy facilities that meet these needs. To achieve this goal this project will extend the already existing NSF-funded LAPPS Grid to include means to access a broad range of interoperable NLP tools, large bodies of publication data and lexical and ontological resources, and, crucially, to rapidly adapt existing software to new domains and evaluate results. This project will also leverage enhancements to the NSF-funded Galaxy platform for interactive data exploration and extended access to NSF hardware resources (XSEDE machines including Stampede, Bridges, and Jetstream). By providing access to services for mining scientific publications and lowering the barriers to entry resulting from licensing, redistribution, and intellectual property concerns, this project provides capabilities that were previously unavailable to scientists. Researchers are able to perform large-scale text mining using an HPC infrastructure through a web-based interface without the need to know about underlying infrastructure. Additionally, providing iterative domain adaptation capabilities enables scientists to easily adapt existing services to specialized areas without configuring or installing additional components. The ability to examine both explicit and implicit information scattered across massive repositories of publications will undoubtedly result in new observations and insights.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.