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

科学出版物的指数增长使得科学家很难跟踪其领域的发展并将不同的进步联系起来。作为回应，人工智能研究人员已经开始开发技术，允许计算机“阅读”科学论文并自动分类主题，提取关键结果，总结贡献，识别联系，并选择可能对每个科学家特别感兴趣的个性化论文集。持久的愿景是建立能够处理大量学术文件并增强人类科学家能力的人工智能系统-加速科学发现并帮助人类快速应对COVID-19大流行等灾难。拟议的语义学者开放数据平台建立基础设施，以支持这项研究，首先收集一套全面的论文，并安排有效的索引。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件处理领域新人的进入门槛，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。拟议中的语义学者开放数据平台有三个部分：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，总结和分析大型和持续更新的文档语料库; 2）一种新的机制，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，包括预先训练的语言模型和用户界面模板，作为研究的基石。这些基础设施将大大降低学术文档处理领域新人的进入门槛，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。幸运的是，最近学术文档处理研究的增加（例如，我们的CORD-19数据集的快速吸收）表明，计算机和信息科学界有兴趣和能力开发新技术，加速科学和帮助应对全球社会挑战，如流行病和气候变化。由此产生的人工智能增强科学发现的进步将使所有科学领域受益，刺激医学进步，创造新的就业机会，并改善盲人研究人员的访问。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。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智力价值和更广泛的影响审查标准进行评估来支持。

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.