C4.5: programs for machine learning
C4.5: programs for machine learning
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ontology Matching
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine Learning Approach for Ontology Mapping Using Multiple Concept Similarity Measures
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Towards a Rule-Based Matcher Selection
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Candidate reduction and alignment improvement techniques used in aligning ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Deciding agent orientation on ontology mappings
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
One size does not fit all: customizing ontology alignment using user feedback
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Composite ontology matching with uncertain mappings recovery
ACM SIGAPP Applied Computing Review
ACM SIGMOD Record
Group decision making in ontology matching
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
A clustering-based approach to ontology alignment
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Rule-based construction of matching processes
Proceedings of the 20th ACM international conference on Information and knowledge management
Improving the accuracy of ontology alignment through ensemble fuzzy clustering
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Aligning large SKOS-Like vocabularies: two case studies
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
Automatic configuration selection using ontology matching task profiling
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus leveraging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the majority vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results.