Mode directed path finding

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Ong, IM (Author) Other; De Castro Dutra, I (Author) Other; Page, D (Author) Other; Costa, VS (Author) FCUP

Conference proceedings International

Title: Machine Learning: ECML 2005

Pages: 673-681

16th European Conference on Machine Learning - ECML 2005

Porto, Portugal, October 3-7, 2005

Indexing

ISI Web of Knowledge - 14 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-007-FGV

DOI: 10.1007/11564096_68

Abstract (EN): Learning from multi-relational domains has gained increasing attention over the past few years. Inductive logic programming (ILP) systems, which often rely on hill-climbing heuristics in learning first-order concepts, have been a dominating force in the area of multi-relational concept learning. However, hill-climbing heuristics are susceptible to local maxima and plateaus. In this paper, we show how we can exploit the links between objects in multi-relational data to help a first-order rule learning system direct the search by explicitly traversing these links to find paths between variables of interest. Our contributions are twofold: (i) we extend the pathfinding algorithm by Richards and Mooney [12] to make use of mode declarations, which specify the mode of call (input or output) for predicate variables, and (ii) we apply our extended path finding algorithm to saturated bottom clauses, which anchor one end of the search space, allowing us to make use of background knowledge used to build the saturated clause to further direct search. Experimental results on a medium-sized dataset show that path finding allows one to consider interesting clauses that would not easily be found by Aleph. © Springer-Verlag Berlin Heidelberg 2005.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9