PFI-TT: A Hybrid Scalable Data Management System Providing Deep Access to the Scientific Knowledge in Data Science

PFI-TT：混合可扩展数据管理系统，提供对数据科学中科学知识的深入访问

• 批准号: 2345794
• 负责人: Michael Gubanov
• 金额: $ 55万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345794&HistoricalAwards=false
• 关键词: PFI; TT; Hybrid; Scalable; Data

The broader impact of this project lies in its transformative potential for accessing scientific knowledge in the field of data science. The data management system, algorithms, and Knowledge Graphs (KGs) will improve the way new advances in data science are searched. By intelligently reformulating the search questions, users will experience a direct path to the most relevant results, significantly reducing the time spent on the usual trial-and-error searches, enhancing the productivity of data scientists and other consumers of scientific knowledge. The potential societal impact will be substantial, as the advantages introduced by the algorithms and systems could lead to broad and significant cost savings in research expenditures for government, companies, and universities. This approach challenges the current de-facto standard of keyword-search and Large Language Models (LLMs). The LLMs are sophisticated artificial intelligence models designed to understand and generate human-like text based on the input they receive. This research promises a more effective and resource-efficient method for accessing scientific knowledge. The project will provide valuable experiential learning for students, fostering skills in technology and entrepreneurship and empowering them to translate their research into successful, student-led, high-tech startups.The project envisions a novel solution to access knowledge in the scientific literature in the form of an intelligent and scalable hybrid data management system. The solution aims to provide users with a user-friendly interface for interacting with scientific concepts, accurately extracted and organized from a myriad of peer-reviewed scientific publications organized in a KG. This KG will surpass traditional approaches, maintained by experts by scale, depth, and freshness of knowledge, continuously learning from the latest peer-reviewed literature. The system's value proposition lies in its potential to achieve time savings of several orders of magnitude compared to current scientific search methods, while also delivering more comprehensive search results. The approach focuses on unsupervised extraction of the main search concepts and their structure from the state-of-the-art publications, organizing them into a hierarchy. This structured approach will simplify access to complex knowledge, enhancing the overall quality of search results and minimizing time spent on the search. The versatility of the approach allows for generalization to other domains, promising broad applicability and impact.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.

该项目更广泛的影响在于其获取数据科学领域科学知识的变革潜力。数据管理系统、算法和知识图（KG）将改进数据科学新进展的搜索方式。通过智能地重新制定搜索问题，用户将体验到获得最相关结果的直接路径，从而显着减少通常的试错搜索所花费的时间，从而提高数据科学家和其他科学知识消费者的生产力。潜在的社会影响将是巨大的，因为算法和系统带来的优势可能会为政府、公司和大学的研究支出带来广泛而显着的成本节省。这种方法挑战了当前关键字搜索和大型语言模型 (LLM) 的事实上的标准。法学硕士是复杂的人工智能模型，旨在根据收到的输入理解并生成类似人类的文本。这项研究有望提供一种更有效、更节约资源的方法来获取科学知识。该项目将为学生提供宝贵的体验式学习，培养技术和创业技能，并使他们能够将研究转化为成功的、学生主导的高科技初创企业。该项目设想了一种新颖的解决方案，用于获取科学文献中的知识智能且可扩展的混合数据管理系统的形式。该解决方案旨在为用户提供一个用户友好的界面，用于与科学概念进行交互，这些科学概念是从知识库中组织的大量同行评审的科学出版物中准确提取和组织的。该知识库将超越由专家维护的传统方法，其知识的规模、深度和新鲜度，不断从最新的同行评审文献中学习。该系统的价值主张在于，与当前的科学检索方法相比，它有可能节省几个数量级的时间，同时还提供更全面的搜索结果。该方法侧重于从最先进的出版物中无监督地提取主要搜索概念及其结构，并将它们组织成层次结构。这种结构化方法将简化对复杂知识的访问，提高搜索结果的整体质量并最大限度地减少搜索时间。该方法的多功能性允许推广到其他领域，有望具有广泛的适用性和影响力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。