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

基本信息

批准号：
2107213
负责人：
Eduard Dragut
金额：
$ 67万
依托单位：
Temple University
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=2107213&HistoricalAwards=false
关键词：
III Medium Collaborative Research Extracting

项目摘要

The goal of this project is to create a framework for linking all salient aspects of an artificial intelligence (AI) workflow, including data, AI models, AI tools, tasks, 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，以链接人工智能（AI）工作流程的所有框架，促进数据，AI工具，任务和培训方法。该工作流程以及因此将为AI的报告办公室e -undtel e -on数据中的Toreems身份证明提供解决方案：“将AI中的打开问题与用于关联数据的框架，模型的框架。”资金机构是研究工具的创建和开放式（例如，数据，模型）。生成它或它解决的任务既不是由模型或任务捕获的，因此，调查人员试图创建构建关系的方法。命名实体识别数据集，AI模型，开发工具和各种方法的名称，例如培训中使用的AI研究工具的方法转弯将产生一种创新的方式，以概念化和执行数据搜索和协议自动识别和目录和Sykey的洞察力的技术是，通过研究论文，AI文物缺乏必要的重复使用。模型是原始的或用作基线的，该项目将为您的研究工作与妇女相关联，并在研究工作中涉及新承诺，以提出新的企业。，Develumt.t他的奖项反映了NSF'SF'Story Mission，并使用基金会的知识分子优点和更广泛的影响标准通过评估来获得支持。