In this paper we explore the range of applicability of abductive reasoning for knowledge discovery. In particular, we discuss a novel form of abduction, called creative abduction, where new knowledge is generated in the process of explaining observed events, and demonstrate its relevance to knowledge discovery. The main contribution of this paper is twofold: First, we show that creative abduction can be used to infer a disposition explaining local temporal regularities. In the presence of multiple correlated regularities, this form abduction may significantly unify a given corpus of knowledge, corresponding to theory formation in scientific discovery. Second, we present a weaker form of creative abduction that infers a goal (e.g. interest) from simple 'condition-effect' rules called 'transitions'. If multiple transitions are correlated, the weaker form of creative abduction can be used to identify, e.g. clusters of Web users, as done in Web usage mining. We will focus on the formal underpinnings of this new form of abduction that seems readily applicable to a wide range of practical knowledge discovery problems.
ASJC Scopus subject areas
- Artificial Intelligence