Graph-Based Acquisition of Expressive Knowledge

Capturing and exploiting knowledge is at the heart of several important problems such as decision making, the semantic web, and intelligent agents. The captured knowledge must be accessible to subject matter experts so that the knowledge can be easily extended, queried, and debugged. In our previous work to meet this objective, we created a knowledgeauthoring system based on graphical assembly from components that allowed acquisition of an interestingly broad class of axioms. In this paper, we explore the question: can we expand the axiom classes acquired by building on our existing graphical methods and still retain simplicity so that people with minimal training in knowledge representation can use it? Specifically, we present techniques used to capture ternary relations, classification rules, constraints, and if-then rules.