LIME: A System for Learning Relations

This paper describes the design of the inductive logic programming system Lime. Instead of employing a greedy covering approach to constructing clauses, Lime employs a Bayesian heuristic to evaluate logic programs as hypotheses. The notion of a simple clause is introduced. These sets of literals may be viewed as subparts of clauses that are eeectively independent in terms of variables used. Instead of growing a clause one literal at a time, Lime eeciently combines simple clauses to construct a set of gainful candidate clauses. Subsets of these candidate clauses are evaluated via the Bayesian heuristic to nd the nal hypothesis. Details of the algorithms and data structures of Lime are discussed. Lime's handling of recursive logic programs is also described. Experimental results to illustrate how Lime achieves its design goals of better noise handling, learning from xed set of examples (and from only positive data), and of learning recursive logic programs are provided. Experimental results comparing Lime with FOIL and PROGOL in the KRK domain in the presence of noise are presented. It is also shown that the already good noise handling performance of Lime further improves when learning recursive deenitions in the presence of noise.