Information Retrieval
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Industrial-strength schema matching
ACM SIGMOD Record
Feature-based survey of model transformation approaches
IBM Systems Journal - Model-driven software development
ACM SIGMOD Record
Semi-automatic model integration using matching transformations and weaving models
Proceedings of the 2007 ACM symposium on Applied computing
Semi-automatic model integration using matching transformations and weaving models
Proceedings of the 2007 ACM symposium on Applied computing
ATL: A model transformation tool
Science of Computer Programming
A survey of schema-based matching approaches
Journal on Data Semantics IV
Bridging grammarware and modelware
MoDELS'05 Proceedings of the 2005 international conference on Satellite Events at the MoDELS
Metamodel matching based on planar graph edit distance
ICMT'10 Proceedings of the Third international conference on Theory and practice of model transformations
SoKNOS: using semantic technologies in disaster management software
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Schema, ontology and metamodel matching - different, but indeed the same?
MEDI'11 Proceedings of the First international conference on Model and data engineering
Model matching for trace link generation in model-driven software development
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
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Data integration is a well-known challenge offering a common view on heterogeneous data, e. g. to ensure consistency or tool interoperability. To implement this integration MDE proposes to apply model transformations. A model transformation requires meta-model matching, i. e. the task of discovering semantic correspondences between elements. Recently, semi-automatic matching has been proposed to support transformation development by mapping generation. However, current meta-model matching approaches concentrate on a fixed non-configurable set of matching algorithms and often miss a thorough evaluation, thus no estimate concerning their quality can be made. We tackle these issues by proposing MatchBox, an approach using a configurable combination of matchers in a common framework. Thereby, we adapt and extend established schema matching techniques for the purpose of meta-model matching. Additionally, we present a benchmark for meta-model matching consisting of 23 real-world model transformations and mappings. This benchmark is used to comprehensively evaluate MatchBox. The results obtained lead to conclusions regarding our approach and the effectiveness of meta-model matching.