Proto-OKN Theme 1: Knowledge Graph to Support Evaluation and Development of Climate Models

Proto-OKN 主题 1：支持气候模型评估和开发的知识图

• 批准号: 2333789
• 负责人: Eduard Dragut
• 金额: $ 149.86万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333789&HistoricalAwards=false
• 关键词: Proto; OKN; Theme; Knowledge; Graph

This Prototype-Open Knowledge Network project on Knowledge Graph to Support Evaluation and Development of Climate Models, aims to create a large multimodal knowledge graph of the most salient aspects of climate modeling, including data, climate models, and tasks. The climate models covered include both classical fluid dynamics models as well as AI-based models. The knowledge graph that is developed will take a holistic view of climate modeling while addressing one of the three key problems identified in the Report of the Office of Science Roundtable on Data for AI, viz., addressing “open questions in AI with frameworks for relating data, models, and tasks.” The climate model knowledge graph will ensure that existing models and datasets are leveraged in new climate modeling undertakings, thereby ensuring that past research investments are reused and fully leveraged in future work. The automated methods developed in this project will help infer paper-data-model-tasks relations, providing the ability to suggest useful, related artifacts to new undertakings thus shortening the time for relevant artifact searches. Successful creation of paper-model-tool relations and embedding of those into a knowledge graph will help provide a structured representation of climate models, making them more easily accessible. The project contributes significantly to the field of information retrieval, with a particular focus on named entity recognition and the creation of a comprehensive knowledge graph. Research papers provide the necessary context for reusing research artifacts, by linking together data, models, and analyses, describing the role of a dataset (e.g., training or testing) and indicating whether a model is original or used as a baseline. Incorporating these insights into a knowledge graph provides researchers an intuitive and structured means for navigating the complex relationships among datasets, models, tools, and methods. This not only facilitates the discovery and reuse of existing research artifacts but also fosters collaboration and innovation in the field research, in this case climate research. Novel deep learning techniques will be developed for automatic entity and relation extraction, entity linking, and construction of a knowledge graph interconnecting these aspects. Novel technology will be developed for automatically identifying and cataloging public climate data and related highly reusable tools. The proposed approach is multimodal, dealing with images as well as tables from text-based publications. This project also has a focus on teaching and training of students at various levels, from high school to doctoral programs.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.

知识图上的原型类型知识网络项目支持气候模型的评估和开发。e多模型的气候模型，包括数据，气候模型，包括基于AI的模型开发的知识图将在AI，即AI的数据圆桌会议上识别出的关键问题之一，即“在AI中开放问题。与新事业相关的工件，用于相关的人工制品搜索。综合知识图，分析，描述数据集的作用（例如，训练或测试）是原始的或使用了ASA的基线。数据集，模型和方法之间的兴奋性。实体链接和结合这些方面的知识图。博士学位。该奖项反映了NSF的遗迹，值得支持Therugh评估，并使用Tounslectul功绩和Roader影响审查标准。