Harmonizing Neural Processing and Symbolic Knowledge Intelligence for Human-Centered Applications

Abstract: Human knowledge emerges through sustained interaction with the physical world and is internalized through two complementary mechanisms: (1) implicit representations encoded in neural parameters and (2) explicit structured formats such as mathematical equations, graphs, charts and tables.

Neural representations offer flexibility and strong statistical generalization but are often opaque and difficult to control. In contrast, structured knowledge explicitly encodes rules and symbolic constraints, enabling interpretable and principled processing, though typically with reduced adaptability. Advancing modern artificial intelligence frameworks requires a principled integration of these two paradigms, leveraging their complementary strengths while mitigating their respective limitations (e.g., neural–symbolic learning, graph machine learning and agentic workflows).

This talk explores how to harmonize neural processing with structured knowledge intelligence, with a particular focus on Retrieval-Augmented Generation (RAG) systems. We address two fundamental questions: (1) How can structured knowledge signals be incorporated into neural RAG pipelines to rigorously guide, constrain and regularize neural processing? (2) How can structured knowledge be systematically abstracted, refined or induced from neural outputs through feedback-driven mechanisms?

To answer these questions, the talk presents a unified framework spanning structured knowledge mining, representation and integration, emphasizing both effectiveness (e.g., mitigating hallucination and enhancing retrieval accuracy) and trustworthiness (e.g., enhancing privacy-preserving and providing an error-bounded guarantee). We illustrate this framework through human-centered applications, including document intelligence, natural science and infrastructure management.

Bio: Yu Wang is a tenure-track assistant professor in the department of Computer Science at the University of Oregon, where he directs the Knowledge Intelligence for Discovery and Decision-Making Lab. Dr. Wang serves as the leading principal investigator on National Science Foundation pilot research projects. His work has received several recognitions, such as the 2025 Special Interest Group on Knowledge Discovery and Data Mining Best Dissertation Award Honorable Mention and the NeurIPS Graph Learning Frontiers Best Paper Award.