Authors:
Oliver Schmidts
1
;
Bodo Kraft
1
;
Ines Siebigteroth
1
and
Albert Zündorf
2
Affiliations:
1
FH Aachen, University of Applied Sciences and Germany
;
2
University of Kassel and Germany
Keyword(s):
Schema Matching, Machine Learning, Classification, Natural Language Processing, Named Entity Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Coupling and Integrating Heterogeneous Data Sources
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
;
Natural Language Interfaces to Intelligent Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
For small to medium sized enterprises matching schemas is still a time consuming manual task. Even expensive commercial solutions perform poorly, if the context is not suitable for the product. In this paper, we provide an approach based on concept name learning from known transformations to discover correspondences between two schemas. We solve schema matching as a classification task. Additionally, we provide a named entity recognition approach to analyze, how the classification task relates to named entity recognition. Benchmarking against other machine learning models shows that when choosing a good learning model, schema matching based on concept name similarity can outperform other approaches and complex algorithms in terms of precision and F1-measure. Hence, our approach is able to build the foundation for improved automation of complex data integration applications for small to medium sized enterprises.