Evaluating Ontology Matchers on Real-World Financial Services Data Models.
Jan Portisch, Michael Hladik, Heiko Paulheim · MADOC (University of Mannheim) · 2019
Financial data in enterprises is often stored using different data models, yet, it needs to be integrated in order to foster comprehensive evaluations. Conceptually, each of those data models can be understood as an ontology, and automated ontology matching can be applied as a first step towards data integration. In this paper, we analyze the performance of existing ontology matching tools for matching financial data models. The data has been provided by SAP SE and consists of real data schemas that are used in the financial services area and mappings between them. We have created five data sets by translating enterprise data schemas to ontologies and expert mappings to ontology alignment gold standards. We evaluate state of the art ontology matchers on our newly created data set. Our experiments show that current matching systems struggle to handle enterprise data sets and achieve significantly lower scores compared to data sets of other evaluation initiatives.