A Note on Two Simple Transformations for Improving the Efficiency of an ILP System

Abstract. We propose a new approach to Inductive Logic Programming 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 defining the hypothesis search space through Inverse Entailment using multiple example seeds.

Section: Introductionmentioning

confidence: 94%

Section: Algorithm 2: Wild Hypothesis Generatormentioning

confidence: 99%

A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment

Bravo¹,

Page²,

Ramakrishnan³

et al. 2005

“…Research in improving the efficiency of ILP systems has thus focused in reducing their sequential execution time, either by reducing the number of generated hypotheses [2,3]; by efficiently testing candidate hypotheses [4,5,6]; or through parallelism [7,8]. Arguably, best results can be achieved through a reduction of the search space.…”

Section: Introductionmentioning

confidence: 99%

On Avoiding Redundancy in Inductive Logic Programming

Fonseca

Costa

Silva

et al. 2004

Self Cite

Abstract. ILP systems induce first-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from finding good models and increases the time to induce them. In this paper we propose a classification of hypotheses redundancy and show how expert knowledge can be provided to an ILP system to avoid it. Experimental results show that the number of hypotheses generated and execution time are reduced when expert knowledge is used to avoid redundancy.

“…Query optimizers transform queries into a different form, which is more efficient to execute. Also in ILP, techniques have been introduced for optimizing first order queries (see [13] for an overview). One specific approach that is popular in relational database management systems is to reorder the relational operators such that the most selective ones are executed first.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 4 we introduce a reordering transformation for optimizing first order queries in ILP. We also describe an extension able to handle queries that are the output of the cut-transform [13]. Section 5 presents experiments that evaluate the performance of the transformation on the Carcinogenesis data set.…”

Section: Introductionmentioning

confidence: 99%

Query Optimization in Inductive Logic Programming by Reordering Literals

Struyf

Blockeel

2003

Abstract. Query optimization is used frequently in relational database management systems. Most existing techniques are based on reordering the relational operators, where the most selective operators are executed first. In this work we evaluate a similar approach in the context of Inductive Logic Programming (ILP). There are some important differences between relational database management systems and ILP systems. We describe some of these differences and list the resulting requirements for a reordering transformation suitable for ILP. We propose a transformation that meets these requirements and an algorithm for estimating the computational cost of literals, which is required by the transformation. Our transformation yields a significant improvement in execution time on the Carcinogenesis data set.