DESKMatcher


Monych, Michael ; Portisch, Jan ; Hladik, Michael ; Paulheim, Heiko


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URL: https://madoc.bib.uni-mannheim.de/58765
Additional URL: http://ceur-ws.org/Vol-2788/oaei20_paper7.pdf
URN: urn:nbn:de:bsz:180-madoc-587652
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: OM 2020 : Proceedings of the 15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference (ISWC 2020) Virtual conference (originally planned to be in Athens, Greece), November 2, 2020
The title of a journal, publication series: CEUR Workshop Proceedings
Volume: 2788
Page range: 181-186
Conference title: OM 2020
Location of the conference venue: Online
Date of the conference: 02.11.2020
Publisher: Shvaiko, Pavel ; Euzenat, Jérôme ; Jiménez-Ruiz, Ernesto ; Hassanzadeh, Oktie ; Trojahn, Cássia
Place of publication: Aachen
Publishing house: RWTH
ISSN: 1613-0073
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Paulheim 2018-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Individual keywords (German): Datenintegration
Keywords (English): ontology matching , data integration , semantic matching
Abstract: This paper describes DESKMatcher, a label-based ontology matcher. It utilizes background knowledge from the financial services and enterprise domain to better find matches in these domains. The background knowledge utilized for the enterprise domain was in the form of documentation of terms used in SAP software (textual). Therefore, Word2Vec and GloVewere used for these corpora. The Financial Industries Business Ontology (FIBO) was used as more specific background knowledge for the financial services domain. Vector space embeddings for this corpus were trained using RDF2Vec and KGloVe. Individual matchers utilizing one set of embeddings (generated from a combination of method and corpus) are pipelined together after string-based matchers, searching only for matches between entities that have not been assignedto a match in a previous step. Results on theOAEI tracks are expectedto be sub-par, because low overlap between corpus and task vocabulary is expected.

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