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.

该项目的目标是创建一个框架来链接人工智能工作流程的所有显着方面，包括数据、人工智能模型、人工智能工具、任务和培训方法。研究人员寻求创建一个全面了解人工智能工作流程的框架，从而为科学办公室人工智能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：“通过关联数据、模型和任务的框架解决人工智能中的开放性问题。”联邦资助机构的关键规定之一是研究成果（例如数据、模型）的创建和公开传播。尽管基于出版物的知识很容易重用，但数据和模型却不然。数据是生成人工智能模型的关键要素。然而，AI 模型与用于生成它的数据或它解决的任务之间的关系，以及测试 AI 模型的数据之间的关系，既不是由模型也不是由数据或任务捕获的。因此，研究人员寻求创建一种统一的方法来构建这种关系并对其进行注释。该项目将为信息检索的广泛领域做出贡献，特别是命名实体识别领域。在这个项目中，命名实体是数据集、人工智能模型、开发工具以及各种方法的名称，例如训练中使用的方法的名称。研究人员将采用整体方法来管理人工智能研究工件，即论文-任务-数据-模型-工具，这反过来将产生一种创新的方法来概念化和执行数据人工智能模型搜索和聚合。该项目的技术创新是创建实体和关系提取以及实体链接的新技术。该项目还将为科学文献挖掘领域做出贡献。研究人员将创建新技术来自动识别和编目公共人工智能数据和模型，以提高其可重用性。关键的见解是，如果没有研究论文本身，研究人工智能工件就缺乏重用的必要背景。例如，论文描述了数据集的作用（例如，训练或测试）并说明模型是原始的还是用作基线。通过自动推断任务-数据-模型关系，该项目将提高向新项目建议工件的能力，从而缩短相关工件搜索的时间。在教育方面，这项工作将涉及对研究生和本科生的培训，特别是鼓励女性和代表性不足的群体参与研究工作和课程开发。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。