The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
Metamodel Matching for Automatic Model Transformation Generation
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
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
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 18th ACM conference on Information and knowledge management
UFOme: An ontology mapping system with strategy prediction capabilities
Data & Knowledge Engineering
Tuning the ensemble selection process of schema matchers
Information Systems
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Semi-automatic schema matching systems have been developed to compute mapping suggestions that can be corrected by a user. However, constructing and tuning match strategies still requires a high manual effort. We therefore propose a self-configuring schema matching system that is able to automatically adapt to the given mapping problem at hand. Our approach is based on analyzing the input schemas as well as intermediate match results. A variety of matching rules use the analysis results to automatically construct and adapt an underlying matching process for a given match task. The evaluation shows that our system is able to robustly return good quality mappings across different mapping problems and domains.