Machine Learning Approach for Ontology Mapping Using Multiple Concept Similarity Measures

Authors:
Ryutaro Ichise
Affiliations:
-
Venue:
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Year:
2008

Citing 0
Cited 5

Evaluation of Similarity Measures for Ontology Mapping

New Frontiers in Artificial Intelligence
Improving Ontology Matching Using Meta-level Learning

ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
An adaptive ontology mapping approach with neural network based constraint satisfaction

Web Semantics: Science, Services and Agents on the World Wide Web
Composite ontology matching with uncertain mappings recovery

ACM SIGAPP Applied Computing Review
A machine learning approach to multilingual and cross-lingual ontology matching

ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new framework for the ontology mapping problem. We organized the ontology mapping problem into a standard machine learning framework, which uses multiple concept similarity measures. We presented several concept similarity measures for the machine learning framework and conducted experiments for testing the framework using real-world data. Our experimental results show that our approach has increased performance with respect to precision, recall and F-measure in comparison with other methods.