III: Medium: Collaborative Research: Extracting and Linking AI Artifacts

III：媒介：协作研究：提取和链接人工智能工件

• 批准号: 2107518
• 负责人: Robert Sloan
• 金额: $ 53万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107518&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Extracting

The goal of this project is to create a framework for linking all salient aspects of an AI workflow, including data, AI model, AI tool, task, and training methodology. The investigators seek to create a framework that takes a holistic view of the AI workflow, and thus, will provide a solution to one of the three key problems identified in the Report of the Office of Science Roundtable on Data for AI: “Address open questions in AI with frameworks for relating data, models, and tasks.” One of the key provisions of federal funding agencies is the creation and open dissemination of research artifacts (e.g., data, models). Although publication-based knowledge is easily reused, data and models are not. Data are the key ingredients to generate AI models. However, the relation between an AI model and the data used to generate it or the task it solves, and the data on which the AI model is tested on, is captured by neither the model nor the data or task. Thus, the investigators seek to create a unified approach to construct this relationship and annotate it. This project will contribute to the broad field of information retrieval and, in particular, to the field of named entity recognition. In this project, the named entities are the datasets, AI models, developing tools, and the names of various methods, such as those employed in training. The investigators will employ a holistic approach to the management of AI research artifacts, i.e., paper-task-data-model-tool, which in turn will produce an innovative way to conceptualize and execute data-AI model search and aggregation. The technical innovation of this project is the creation of novel techniques for entity and relation extraction as well as for entity linking. The project will also contribute to the field of scientific literature mining. The investigators will create novel technology to automatically identify and catalog public AI data and models that increase their reusability. The key insight is that, without the research papers themselves, the research AI artifacts lack the necessary context for reuse. For example, papers describe the role of a dataset (e.g., training or testing) and tell if a model is original or used as a baseline. By automatically inferring task-data-model relations, this project will increase the ability of suggesting artifacts to a new undertaking, thus shortening the time for relevant artifact search. Educationally, this work will involve training of graduate and undergraduate students, particularly encouraging the participation of women and underrepresented groups in the research efforts, and curriculum development.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.

该项目的目的是创建一个框架，以链接AI工作流的所有显着方面，包括数据，AI模型，AI工具，任务和培训方法。调查人员试图创建一个对AI工作流程的全面看法的框架，因此，将为AI的数据圆桌会议报告中确定的三个关键问题之一提供解决方案：“将AI中的“与数据，模型和任务相关的框架”中的框架中的开放问题解决。”联邦资助机构的关键规定之一是创建和开放研究工具（例如，数据，模型）。尽管基于出版物的知识很容易重复使用，但数据和模型却不是。数据是生成AI模型的关键归纳。但是，AI模型与用于生成它或其解决的任务的数据与AI模型的数据之间的关系是由模型或数据或任务捕获的。这是，调查人员试图创建一种统一的方法来构建这种关系并注释它。该项目将有助于信息检索的广泛领域，尤其是命名实体识别的领域。在这个项目中，指定的实体是数据集，AI模型，开发工具以及各种方法的名称，例如培训中使用的方法。调查人员将采用一种整体方法来管理AI研究工具，即纸质任务模型工具，这反过来又将产生一种创新的方式来概念化和执行Data-AI模型搜索和聚合。该项目的技术创新是为实体和关系提取的新技术以及实体链接的创造。该项目还将为科学文献挖掘的领域做出贡献。研究人员将创建新颖的技术，以自动识别和分类公共AI数据和增加其可重复性的模型。关键的见解是，没有研究论文本身，AI伪像的研究缺乏重用的必要背景。例如，论文描述了数据集（例如培训或测试）的作用，并确定模型是原始的还是用作基线的。通过自动推断任务数据模型的关系，该项目将增加向新事业提出工件的能力，从而缩短相关伪像搜索的时间。在教育上，这项工作将涉及培训研究生和本科生，特别是鼓励妇女和代表性不足的群体参与研究工作，以及课程发展。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来获得的支持。