Relational Instance-Based Learning with Lists and Terms

The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance-based learner RIBL that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and empirically demonstrate its additional power on a problem from the domain of biochemistry. The paper also includes a thorough reconstruction of RIBL'S overall algorithm.