Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
The nature of statistical learning theory
The nature of statistical learning theory
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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The emerging Semantic Web relies on the development of ontologies and the deployment of data annotated by ontologies. For a certain domain with a suitable ontology developed, its ontology annotated data (or simply ontology data) from different sources is often overlapping. Similar to a data warehousing process that transforms and merges data from different databases, an integration over the Semantic Web data sources needs to match relevant ontology data among them. This study develops a matching method to address the issue of ontology data matching. This method is different from other data matching or merging methods applied to database or data warehouse cleansing in that it employs more similarity measurements by exploring ontology features. Our experiments show that this proposed method increases matching accuracy.