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

第一波人工智能被称为符号人工智能，专注于显性知识。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了，它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，并使下一代数据处理成为可能，例如语义搜索。现在，我们预示着第三波人工智能的到来，它建立在所谓的神经符号方法的基础上，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各自的能力和好处是特别有吸引力的。这导致了一种我们称之为知识注入（深度）学习的方法的发展。这个项目将推进目前有限的结合知识图和深度学习的形式，称为浅扩散和半扩散，以及更高级的称为深度注入的形式，这将支持在深度学习架构中的不同抽象层次上更多种知识的更强交错。本项目将从两个方面研究深度知识注入策略。第一种是在深度学习管道中注入来自知识图的不同类型的知识。例如，在自然语言处理中，我们将研究语言学、常识、基础广泛和特定领域知识的结合。第二种方法是注入表示不同抽象级别的分层知识，例如关注对象的物理特征的原始数据测量中包含的低级抽象，以及捕获对象的更多概念性方面（例如对象在应用程序中的功能）的高级抽象。每个深度网络层可以采用不同类型的知识，表示该层的预期抽象级别。例如，在变压器中，我们可以重新参数化不同的变压器块（层），这样变压器块将采用不同类型的知识来表示该层的预期抽象级别。此外，深度注入管道可以从适当层的知识图中为深度学习管道的结果生成解释，从而清晰地显示输入部分之间的上下文连接。分层抽象和解释模块都将对提高机器智能状态做出重要贡献。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。

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.