Optimizing chain datalog programs and their inference procedures

Abstract

We present methods for optimizing chain Datalog programs by restructuring and post-processing. The rules of the programs define intensionally a set of target concepts, which are to be derived via forward chaining. The restructuring methods transform the rules, such that redundancies and ambiguities, which prevent efficient evaluations, are removed without changing the coverage of the target concepts. The post-processing method increases the coverage by introducing recursive rules in the chain Datalog program. Based on the correspondence between chain Datalog programs and context-free languages, which in our case reduce to regular ones, we present a method to map restructured and/or post-processed programs to prefix acceptors, which are deterministic finite state automata, whose input/output alphabets consist of predicates. We present an efficient marker passing method which is applied to a prefix acceptor, and which optimizes inferences. We proof that this method is sound and complete, i.e., it calculates the minimum Herbrand model of the chain Datalog program which has been mapped to the respective prefix acceptor. As the developments, presented in this paper, have been motivated by an ILP application to robotics, we have applied the methods to this real-world domain. The experimental results at the end of the paper reflect the improvements, we have gained. The paper is written in English.