Paper Info
Title
Mode directed path finding
Abstract
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.
Year
DOI
Venue
2005
10.1007/11564096_68
ECML
Keywords
Field
DocType
medium-sized dataset show,multi-relational domain,multi-relational concept learning,path finding,extended path,first-order rule,first-order concept,search space,direct search,multi-relational data,hill-climbing heuristics,concept learning,hill climbing,first order,relational data
Pathfinding,Inductive logic programming,Computer science,Hypergraph,Mode (statistics),Concept learning,Directed graph,Maxima and minima,Heuristics,Artificial intelligence,Distributed computing
Conference
Volume
ISSN
ISBN
3720
0302-9743
3-540-29243-8
Citations 
PageRank 
References 
10
0.60
10
Authors
4
Name
Order
Citations
PageRank
Irene M. Ong111612.64
Inês de Castro Dutra210411.46
David Page353361.12
Vítor Santos Costa488074.70