EAGER: Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning

EAGER：通过深度知识注入学习推进神经符号人工智能

• 批准号: 2133842
• 负责人: Amit Sheth
• 金额: $ 14万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133842&HistoricalAwards=false
• 关键词: EAGER; Advancing; Neuro; symbolic; AI

The first wave of AI termed symbolic AI, focused on explicit knowledge. The current second wave of AI is termed statistical AI. The deep learning techniques have been able to exploit large amounts of data and massive computational power to improve upon human levels of performance in narrowly defined tasks. Separately, knowledge graphs emerged as a powerful tool to capture and exploit an extensive amount and variety of explicit knowledge to make algorithms better understand the content, and enable the next generation of data processing, such as in semantic search. Now, we herald towards the third wave of AI built on what is termed as the neuro-symbolic approach that combines the strengths of statistical and symbolic AI. Combining the respective powers and benefits of using knowledge graphs and deep learning is particularly attractive. This has led to the development of an approach we have called knowledge-infused (deep) learning. This project will advance the currently limited forms of combining the knowledge graphs and deep learning, called shallow and semi-diffusion, with a more advanced form called deep-infusion, that will support stronger interleaving of more variety of knowledge at different levels of abstraction with layers in a deep learning architecture.This project will investigate the deep knowledge-infusion strategy in two substantial ways. The first is to infuse knowledge of different types from knowledge graphs in the deep learning pipeline. For example, in natural language processing, we will investigate the incorporation of linguistic, common sense, broad-based and domain-specific knowledge. The second is to infuse stratified knowledge representing different levels of abstractions, such as low levels of abstractions contained in raw data measurements that focus on the physical features of an object, and higher levels of abstractions that capture more conceptual aspects of the object, such as the object's functionality in an application. Each deep network layer may take a different type of knowledge representing the intended level of abstraction at that layer. For example in a transformer, we can reparameterize different transformer blocks (layers) such that the transformer block will take a different type of knowledge representing the intended level of abstraction at that layer. Furthermore, the deep infusion pipeline can generate explanations for the outcomes of the deep-learning pipeline from the knowledge graph at the appropriate layer leading to a clear picture of the contextual connection between parts of the input. Both layered abstraction and explanation modules would be highly significant contributions towards improving the state of machine intelligence.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.

第一波人工智能被称为符号人工智能，专注于显式知识。目前的第二波人工智能被称为统计人工智能。深度学习技术已经能够利用大量的数据和巨大的计算能力来提高人类在狭义任务中的表现水平。另外，知识图作为一种强大的工具出现，可以捕获和利用大量和各种显式知识，使算法更好地理解内容，并实现下一代数据处理，例如语义搜索。现在，我们迎来了第三次人工智能浪潮，它建立在所谓的神经符号方法之上，结合了统计和符号人工智能的优势。结合使用知识图和深度学习的各自功能和优势特别有吸引力。这导致了我们称之为知识注入（深度）学习的方法的发展。该项目将推进知识图谱和深度学习相结合的有限形式，称为浅扩散和半扩散，以及更高级的形式称为深度注入，这将支持不同抽象级别的更多种类的知识与深度学习架构中的层进行更强的交织。该项目将从两个方面研究深度知识注入策略。第一个是将来自知识图谱的不同类型的知识注入深度学习管道。例如，在自然语言处理中，我们将研究语言，常识，广泛和特定领域知识的结合。第二个是注入分层的知识，表示不同层次的抽象，如低层次的抽象包含在原始数据的测量，专注于一个对象的物理特征，和更高层次的抽象，捕捉更多的概念方面的对象，如对象的功能在应用程序中。每个深度网络层可以采用表示该层处的预期抽象级别的不同类型的知识。例如，在Transformer中，我们可以重新参数化不同的Transformer块（层），以便Transformer块将采用不同类型的知识，表示该层的预期抽象级别。此外，深度注入管道可以在适当的层从知识图生成对深度学习管道的结果的解释，从而清楚地了解输入部分之间的上下文连接。分层抽象和解释模块都将对改善机器智能状态做出非常重要的贡献。该奖项反映了NSF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。

项目成果

Tutorial: Causal AI for Web and Health Care.

教程：网络和医疗保健的因果人工智能。

• DOI: 10.1145/3543873.3587713
• 发表时间: 2023
• 期刊: Companion Proceedings of the ACM Web Conference
• 作者: Usha Lokala, Kaushik Roy

A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework

Reddit 上了解心理健康的计算方法：知识感知多任务学习框架

• DOI: 10.1609/icwsm.v16i1.19322
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Lokala, Usha;Srivastava, Aseem;Dastidar, Triyasha Ghosh;Chakraborty, Tanmoy;Akhtar, Md Shad;Panahiazar, Maryam;Sheth, Amit
• 通讯作者: Sheth, Amit

Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts

• DOI: 10.18653/v1/2022.clpsych-1.12
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Shrey Gupta;Anmol Agarwal;Manas Gaur;Kaushik Roy;Vignesh Narayanan;P. Kumaraguru;Amit P. Sheth

CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning

• DOI: 10.1109/mic.2021.3133551
• 发表时间: 2022-01
• 期刊: IEEE Internet Computing
• 影响因子: 3.2
• 作者: Utkarshani Jaimini;A. Sheth

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case

演示 Alleviate：展示人工智能支持的远程医疗虚拟援助：心理健康案例

• DOI: 10.1609/aaai.v37i13.27085
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Roy, Kaushik;Khandelwal, Vedant;Goswami, Raxit;Dolbir, Nathan;Malekar, Jinendra;Sheth, Amit
• 通讯作者: Sheth, Amit