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

这个原型是关于支持气候模型评估和开发的知识图的开放式知识网络项目，旨在创建一个大型的多模式知识图，其中包括气候建模最突出的方面，包括数据、气候模型和任务。涵盖的气候模型既包括经典的流体动力学模型，也包括基于人工智能的模型。开发的知识图谱将从整体上看待气候建模，同时解决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，即解决“人工智能中的公开问题，以及相关数据、模型和任务的框架。”气候模型知识图谱将确保在新的气候模拟工作中利用现有的模型和数据集，从而确保在今后的工作中重复使用和充分利用过去的研究投资。该项目开发的自动化方法将有助于推断纸张-数据-模型-任务的关系，提供向新项目建议有用的、相关的人工制品的能力，从而缩短相关人工制品搜索的时间。成功地创建纸-模型-工具关系并将其嵌入知识图谱将有助于提供气候模型的结构化表示，使其更容易获取。该项目对信息检索领域作出了重大贡献，特别侧重于识别命名实体和创建一个全面的知识图谱。研究论文通过将数据、模型和分析链接在一起，描述数据集的作用(例如，培训或测试)，并指出模型是原始的还是用作基线，为重用研究构件提供了必要的上下文。将这些洞察力结合到知识图谱中，为研究人员提供了一种直观和结构化的方法，用于导航数据集、模型、工具和方法之间的复杂关系。这不仅促进了现有研究成果的发现和再利用，还促进了实地研究中的合作和创新，在这种情况下是气候研究。新的深度学习技术将被开发用于自动提取实体和关系、实体链接以及构建相互连接这些方面的知识图谱。将开发用于自动识别和编目公共气候数据和相关高度可重复使用的工具的新技术。拟议的方法是多模式的，既处理基于文本的出版物的图像，也处理表格。该项目还侧重于从高中到博士项目的不同层次的学生的教学和培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.