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

这个关于支持气候模型评估和开发的知识图谱的原型开放知识网络项目，旨在创建气候建模最突出方面的大型多模态知识图谱，包括数据、气候模型和任务。所涉及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角度看待气候建模，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“人工智能中与数据、模型和任务相关的框架的开放性问题”。气候模型知识图谱将确保现有模型和数据集在新的气候建模工作中得到利用，从而确保过去的研究投入在未来的工作中得到重用和充分利用。在这个项目中开发的自动化方法将有助于推断纸张-数据-模型-任务之间的关系，提供向新项目建议有用的、相关的工件的能力，从而缩短相关工件搜索的时间。成功地创建纸-模型-工具关系，并将其嵌入到知识图谱中，将有助于提供气候模型的结构化表示，使它们更容易获取。该项目对信息检索领域做出了重大贡献，特别关注命名实体识别和全面知识图谱的创建。研究论文通过将数据、模型和分析联系在一起，描述数据集的角色（例如，训练或测试），并指出模型是原始的还是用作基线，为重用研究工件提供了必要的上下文。将这些见解整合到知识图中，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，而且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用于自动实体和关系提取、实体链接以及连接这些方面的知识图的构建。将开发新技术，自动识别和编目公共气候数据和相关的高度可重复使用的工具。所提出的方法是多模式的，既处理图像，也处理基于文本的出版物中的表。该项目还侧重于从高中到博士课程的各级学生的教学和培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.