Evaluating ontology matchers on real-world financial services data models

Portisch, Jan ; Hladik, Michael ; Paulheim, Heiko

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URL:	https://madoc.bib.uni-mannheim.de/52021
Weitere URL:	http://ceur-ws.org/Vol-2451/paper-22.pdf
URN:	urn:nbn:de:bsz:180-madoc-520218
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2019
Buchtitel:	SEMPDS 2019 : Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019) Karlsruhe, Germany, September 9th to 12th, 2019
Titel einer Zeitschrift oder einer Reihe:	CEUR Workshop Proceedings
Band/Volume:	2451
Seitenbereich:	1-5
Veranstaltungstitel:	SEMANTiCS 2019
Veranstaltungsort:	Karlsruhe, Germany
Veranstaltungsdatum:	09.-12.09.2019
Herausgeber:	Alam, Mehwish ; Usbeck, Ricardo ; Pellegrini, Tassilo ; Sack, Harald ; Sure-Vetter, York
Ort der Veröffentlichung:	Aachen, Germany
Verlag:	RWTH Aachen
ISSN:	1613-0073
Verwandte URLs:	http://ceur-ws.org/Vol-2451/
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Fachgebiet:	004 Informatik 330 Wirtschaft
Freie Schlagwörter (Deutsch):	Datenintegration , Datenmanagement , Ontologiematching
Freie Schlagwörter (Englisch):	ontology alignment , ontology matching , data integration , data management
Abstract:	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.