Abstract

Abstract
Inductive Logic Programming (ILP) is an established subfield of Machine Learning. Nevertheless, it is recognized that efficiency and scalability is a major obstacle to an increased usage of ILP systems in complex applications with large hypotheses spaces. In this work, we focus on improving the efficiency and scalability of ILP systems by exploring tabling mechanisms available in the underlying Logic Programming systems. Tabling is an implementation technique that improves the declarativeness and performance of Prolog systems by reusing answers to subgoals. To validate our approach, we ran the April ILP system in the YapTab Prolog tabling system using two well-known datasets. The results obtained show quite impressive gains without changing the accuracy and quality of the theories generated.

Abstract

Abstract
The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.

Abstract
A critical issue in the design of logic programming systems is their memory performance, both in terms of total memory usage and locality in memory accesses. BEAM, as most modern Prolog systems, requires both good emulator design and good memory performance for best performance. We report on a detailed study of the memory management techniques used on our sequential implementation of the EAM. We address questions like how effective are the techniques the BEAM uses to recover and reuse memory space, how garbage collection affects performance and how to classify and unify variables in a EAM environment. We also propose a finer variable allocation scheme to reduce memory overheads that is quite effective at reducing memory pressure, with only a small overhead.

Abstract
We propose a new approach to Inductive Logic Programming i that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for denning the hypothesis search space through Inverse Entailment using multiple example seeds. © Springer-Verlag Berlin Heidelberg 2005.