An approach to parallel class expression learning

Authors:
An C. Tran;Jens Dietrich;Hans W. Guesgen;Stephen Marsland
Affiliations:
School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand;School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand;School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand;School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand
Venue:
RuleML'12 Proceedings of the 6th international conference on Rules on the Web: research and applications
Year:
2012

Citing 14
Cited 0

Inductive logic programming

New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Introduction to algorithms

Introduction to algorithms
Inductive Logic Programming: Techniques and Applications

Inductive Logic Programming: Techniques and Applications
An algorithm based on counterfactuals for concept learning in the semantic web

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
MapReduce: simplified data processing on large clusters

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pellet: A practical OWL-DL reasoner

Web Semantics: Science, Services and Agents on the World Wide Web
Building rules on top of ontologies for the semantic web with inductive logic programming

Theory and Practice of Logic Programming
DL-FOIL Concept Learning in Description Logics

ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Concept learning in description logics using refinement operators

Machine Learning
DL-Learner: Learning Concepts in Description Logics

The Journal of Machine Learning Research
Lattice-search runtime distributions may be heavy-tailed

ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Use cases for abnormal behaviour detection in smart homes

ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Class expression learning for ontology engineering

Web Semantics: Science, Services and Agents on the World Wide Web
Inductive learning for the Semantic Web: What does it buy?

Semantic Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a Parallel Class Expression Learning algorithm that is inspired by the OWL Class Expression Learner (OCEL) and its extension --- Class Expression Learning for Ontology Engineering (CELOE) --- proposed by Lehmann et al. in the DL-Learner framework. Our algorithm separates the computation of partial definitions from the aggregation of those solutions to an overall complete definition, which lends itself to parallelisation. Our algorithm is implemented based on the DL-Learner infrastructure and evaluated using a selection of datasets that have been used in other ILP systems. It is shown that the proposed algorithm is suitable for learning problems that can only be solved by complex (long) definitions. Our approach is part of an ontology-based abnormality detection framework that is developed to be used in smart homes.