Computablizing Human Knowledge for Machines Through Domain-Specific Representation

PhD Qualifying Examination


Title: "Computablizing Human Knowledge for Machines Through Domain-Specific 
Representation"

by

Mr. Yuzhe SHI


Abstract:

Domain practitioners working in critical sectors, including scientific 
discovery, engineering innovation, and social governance, are addressing 
frontier challenges within their respective domains by leveraging 
Domain-Specific Representations (DSRs) that render domain knowledge 
computable for Artificial Intelligence (AI) solutions. However, as domain 
practitioners craft DSRs tailored exclusively to their particular areas of 
focus, the diverse domain contexts and the methodologies of solving problems 
with that knowledge become intertwined. This results in fragmented, 
case-specific solutions with limited established practices to guide further 
research and development from a holistic AI application ecosystem viewpoint. 
This survey presents the first systematic analysis of DSR-AI approaches, 
disentangling domain context and methodology through a two-dimensional 
framework: (i) the functions of human knowledge, categorized according to 
Bayesian theory as the cognitive foundation, namely, knowledge with 
generative, discriminative, and descriptive functions; and (ii) the 
mechanisms by which human knowledge is utilized in AI, encompassing 
retrieval, modularization, composition, decomposition, planning, 
optimization, and explanation. Through this analytical framework, we 
illuminate the landscape, identify key challenges, and highlight research 
opportunities of knowledge utilization across domains, while elucidating 
shared methodologies for designing, deploying, and communicating DSRs to 
democratize AI for a broader spectrum of domains.


Date:                   Tuesday, 26 August 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Huamin Qu (Supervisor)
                        Dr. Shuai Wang (Chairperson)
                        Dr. Jiasi Shen