A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Model independent assertions for integration of heterogeneous schemas
The VLDB Journal — The International Journal on Very Large Data Bases
Machine Learning
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Semantic Integration in Heterogeneous Databases Using Neural Networks
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Autoplex: Automated Discovery of Content for Virtual Databases
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
THALIA: Test Harness for the Assessment of Legacy Information Integration Approaches
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Integration of XML schemas at various "severity" levels
Information Systems
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology Matching
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Quickmig: automatic schema matching for data migration projects
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
XBenchMatch: a benchmark for XML schema matching tools
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Managing Uncertainty in Schema Matcher Ensembles
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A large scale taxonomy mapping evaluation
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A survey of schema-based matching approaches
Journal on Data Semantics IV
The generation Y of XML schema matching panel description
XSym'07 Proceedings of the 5th international conference on Database and XML Technologies
Tuning the ensemble selection process of schema matchers
Information Systems
FORUM: a flexible data integration system based on data semantics
ACM SIGMOD Record
A generic approach for combining linguistic and context profile metrics in ontology matching
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Non-binary evaluation for schema matching
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Increasing recall of process model matching by improved activity label matching
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). In this article, we present a novel method for selecting the most appropriate schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.