AND Parallelism for ILP: The APIS System

Abstract

Inductive Logic Programming (ILP) is a well known approach to Multi-Relational Data Mining. ILP systems may take a long time for analyzing the data mainly because the search (hypotheses) spaces are often very large and the evaluation of each hypothesis, which involves theorem proving, may be quite time consuming in some domains. To address these efficiency issues of ILP systems we propose the APIS (And ParallelISm for ILP) system that uses results from Logic Programming AND-parallelism. The approach enables the partition of the search space into sub-spaces of two kinds: sub-spaces where clause evaluation requires theorem proving; and sub-spaces where clause evaluation is performed quite efficiently without resorting to a theorem prover. We have also defined a new type of redundancy (Coverage-equivalent redundancy) that enables the prune of significant parts of the search space. The new type of pruning together with the partition of the hypothesis space considerably improved the performance of the APIS system. An empirical evaluation of the APIS system in standard ILP data sets shows considerable speedups without a lost of accuracy of the models constructed.

A logic-based relational learning approach to relation extraction: The OntoILPER system

“…We will also investigate ILP-based rule induction from larger datasets aiming to promote OntoILPER scalability. Such improvement is possible, for example, by sampling techniques, for selecting the most informative examples and removing the redundant ones [Byrd et al, 2012]; and parallel ILP learning processing [Camacho et al, 2014[Camacho et al, , 2016 [Srinivasan et al, 2012] that can decompose the learning problem into smaller more manageable parts. Another future line of study concerns adapting OntoILPER for performing Event Extraction that aims at identifying n-ary relations in the biomedical domain [Björne and Salakoski, 2015].…”

OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

“…We will also investigate ILP-based rule induction from larger datasets aiming to promote OntoILPER scalability. Previous work for promoting scalability in ILP-based rule learning includes sampling techniques, for only selecting the most informative examples and removing the redundant ones [49]; and parallel ILP processing [50] [51] that can decompose the learning problem into smaller more manageable parts.…”