MDMapper: A Framework for Aligning Master Data Models using Ontology Matching Techniques

This paper introduces a matching framework tailored for master data model matching, incorporating techniques from the field of ontology matching. We present a new quantitative approach for heuristic similarity estimation between hierarchical data structures, which involves heterogeneous data. We also introduce a relation-based navigation technique and an availability management method based on restrictions that support efficient and progressive matching processes. This integration of ontology matching techniques into master data model matching not only improves alignment consistency and quality, but also facilitates more automatic data exchange solutions. The experiments on OAEI Anatomy and Conference tracks indicate that our approach may be competitive, while an experiment on industrial classification standards shows that our approach performs significantly better than the considered baseline approaches.