CCRI: Research Infrastructure: NEW: Semantic Scholar Open Data Platform: Enabling Research Into Scientific Search and Discovery

CCRI：研究基础设施：新：语义学者开放数据平台：促进科学搜索和发现研究

• 批准号: 2213656
• 负责人: Daniel Weld
• 金额: $ 200万
• 依托单位: THE ALLEN INSTITUTE FOR ARTIFICIAL INTELLIGENCE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213656&HistoricalAwards=false
• 关键词: CCRI; Research; Infrastructure; NEW; Semantic

The exponential growth of scientific publication makes it difficult for scientists to track developments in their field and make connections between different advances. In response, artificial-intelligence researchers have started to develop techniques that allow computers to ‘read’ scientific papers and automatically classify topics, extract key results, summarize contributions, identify connections, and select a personalized set of papers that may be of special interest to each scientist. The enduring vision is to build AI systems that can process an immense corpus of scholarly documents and augment the capabilities of human scientists – accelerating scientific discovery and helping humanity quickly confront disasters such as the COVID-19 pandemic. The proposed Semantic Scholar Open Data Platform builds infrastructure to support this research by first gathering a comprehensive set of papers and arranging for efficient indexing. The system processes PDF-formatted papers to extract information and use advanced analytic processing approaches to provide researchers access to results. The infrastructure will dramatically lower the barrier to entry for newcomers to the field of scholarly document processing, improve reproducibility of experiments, and accelerate innovation in the important area of AI-augmented scientific discoveryThe infrastructure proposed is unique, because alternative sources of academic papers are either closed, incomplete, have limited programmatic access, or have been retired. The proposed Semantic Scholar Open Data Platform has three parts: 1) a comprehensive set of online services enabling researchers to programmatically search, filter, extract, summarize, and analyze a large and continually-updated corpus of documents; 2) a new mechanism that enables researchers to curate their own domain-specific text corpora, as the team previously created the CORD-19 dataset for coronavirus research; 3) open source software, including pretrained language models and user interface templates to serve as research building blocks. Together the infrastructure will dramatically lower the barrier to entry for newcomers to the field of scholarly document processing, improve reproducibility of experiments, and accelerate innovation in the important area of AI-augmented scientific discovery. Fortunately, the recent increase in research in scholarly document processing (e.g., the rapid uptake of our CORD-19 dataset) shows that the computer and information science community has the interest and capability to develop new technologies that accelerate science and help meet global societal challenges, such as pandemics and climate change. The resulting advances in AI-augmented scientific discovery will benefit all areas of science, spurring medical advances, creating new jobs, and improving access for blind researchers. We will improve global infrastructure by providing open services, data sets, code, and associated educational materials. The team will also engage with underrepresented STEM students and through K-12 outreach.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.

科学出版物的指数级增长使科学家很难跟踪其领域的发展并在不同的进步之间建立联系。为此，人工智能研究人员已开始开发技术，使计算机能够“阅读”科学论文并自动对主题进行分类，提取关键结果，总结贡献，识别联系，并选择每位科学家可能特别感兴趣的一组个性化论文。持久的愿景是构建能够处理大量学术文档并增强人类科学家能力的人工智能系统，从而加速科学发现并帮助人类快速应对诸如 COVID-19 大流行等灾难。拟议的语义学者开放数据平台通过首先收集一套全面的论文并安排有效的索引来构建基础设施来支持这项研究。该系统处理 PDF 格式的论文以提取信息，并使用先进的分析处理方法为研究人员提供结果。该基础设施将大大降低学术文档处理领域新手的进入门槛，提高实验的可重复性，并加速人工智能增强科学发现这一重要领域的创新。所提出的基础设施是独一无二的，因为学术论文的替代来源要么是封闭的、不完整的，要么是程序化访问有限的，要么是已经退役的。拟议的语义学者开放数据平台由三个部分组成：1）一套全面的在线服务，使研究人员能够以编程方式搜索、过滤、提取、总结和分析大量且不断更新的文档语料库； 2）一种新机制，使研究人员能够管理自己的特定领域文本语料库，因为该团队之前为冠状病毒研究创建了 CORD-19 数据集； 3）开源软件，包括预训练的语言模型和用户界面模板，作为研究构建模块。这些基础设施将大大降低学术文档处理领域的新手进入门槛，提高实验的可重复性，并加速人工智能增强科学发现这一重要领域的创新。幸运的是，最近学术文档处理研究的增加（例如，我们的 CORD-19 数据集的快速采用）表明计算机和信息科学界有兴趣并有能力开发新技术，以加速科学发展并帮助应对全球社会挑战，例如流行病和气候变化。人工智能增强科学发现所带来的进步将惠及所有科学领域，促进医学进步，创造新的就业机会，并改善盲人研究人员的机会。我们将通过提供开放服务、数据集、代码和相关教育材料来改善全球基础设施。 The team will also engage with underrepresented STEM students and through K-12 outreach.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.