III: Small: Intelligent Scientific Text Analytics with Knowledge-Augmented Abductive Reasoning

科学家们每年都会发表大量的研究论文和专利，以促进我们对世界和宇宙的理解。与此同时，他们正在大力开发工具来提高生产力。虽然这些工具能够处理科学文本，但它们并不具备像科学家那样思考或写作的智能，以帮助他们的工作。自然语言生成系统可能会生成一些新的语句，这些语句读起来很流畅，很难与人类书写的文本区分开来。然而，由于缺乏对科学创新的推理能力，现有的系统并不像与人类研究助理一起工作那样智能或可靠。该项目旨在使比较推理在一个新的智能系统的科学文本分析，这是在现有的系统中缺失。比较推理通过将某事物与另一事物进行比较来确定该事物的重要性。比较推理在科学创新中起着核心作用，在人工智能的背景下可以归类为溯因推理。该项目将设计和开发新的文本生成方法，用于科学溯因推理和智能科学文本分析。此外，这项研究将支持一批博士，本科生和高中生的专业发展。该项目的技术目标分为三个推力。第一个开发和比较自然语言生成模型的基础上的数据驱动的架构和一个新的架构的启发和植根于溯因理论。这些模型将在科学领域的比较总结和比较论证生成任务上进行评估。第二个推力设计检索增强的方法与异构的知识源，如表，分类法，知识图，以提高科学溯因推理模型的性能。由于检索和编码每个实例可能非常耗时，第三个推力构建知识记忆网络，学习和管理来自知识源的科学概念和关系的分布式表示。当各种科学源数据规模较大时，它们将加速检索增强。最后，这些技术将被集成到一个新的人工智能系统中，该系统可以准确地生成解释性句子，以自动进行比较推理，并协助科学创新。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。

Scientists are producing vast numbers of research articles and patents every year to advance our understanding of our world and the universe. Meanwhile, they are making a great effort to build tools to boost their productivity. While these tools are able to process scientific text, they are not endowed with intelligence to think or write like scientists to help their work. Natural language generation systems may generate some new statements that are fluent to read and hard to distinguish from human-written texts. However, existing systems are not as intelligent or reliable as working with human research assistants due to lack of reasoning abilities about scientific innovation. This project aims to enable comparative reasoning in a novel intelligent system of scientific text analytics, which is missing in existing systems. Comparative reasoning establishes the importance of something by comparing it against something else. Comparative reasoning plays a central role in scientific innovation and can be categorized as abductive reasoning in the context of artificial intelligence. This project will design and develop novel text generation approaches for scientific abductive reasoning and intelligent scientific text analytics. Moreover, this research will support the professional development of a cohort of PhD, undergraduate, and high school students.The technical aims of the project are divided into three thrusts. The first develops and compares natural language generation models based on a data-driven architecture and a novel architecture inspired and rooted in theories of abduction. These models will be evaluated on the tasks of comparative summarization and comparative argument generation in scientific domains. The second thrust designs retrieval-augmented approaches with heterogeneous knowledge sources such as tables, taxonomies, and knowledge graphs to improve the performance of scientific abductive reasoning models. Because retrieving and encoding every instance can be very time consuming, the third thrust builds knowledge memory networks that learns and manages distributed representations of scientific concepts and relations from the knowledge sources. They will accelerate the retrieval augmentation, when all the types of scientific source data are of large scale. Finally, these techniques will be integrated into a new artificial intelligence system that accurately generates explanatory sentences to automate comparative reasoning and assist scientific innovation.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.