Intensional question answering using ILP: What does an answer mean?

Abstract

We present an approach for computing intensional answers given a set of extensional answers returned as a result of a user query to an information system. Intensional answers are considered as descriptions of the actual answers in terms of properties they share and which can enhance a user's understanding of the answer itself but also of the underlying knowledge base. In our approach, an intensional answer is represented by a clause and computed based on Inductive Logic Programming (ILP) techniques, in particular bottom-up clause generalization. The approach is evaluated in terms of usefulness and time performance, and its potential for helping to detect flaws in the knowledge base is discussed. While the approach is used in the context of a natural language question answering system in our setting, it clearly has applications beyond. © 2008 Springer-Verlag Berlin Heidelberg.