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DESKMatcher
Monych, Michael
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Portisch, Jan
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Hladik, Michael
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Paulheim, Heiko
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URL:
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https://madoc.bib.uni-mannheim.de/58765
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Additional URL:
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http://ceur-ws.org/Vol-2788/oaei20_paper7.pdf
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URN:
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urn:nbn:de:bsz:180-madoc-587652
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Document Type:
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Conference or workshop publication
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Year of publication:
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2020
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Book title:
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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
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The title of a journal, publication series:
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CEUR Workshop Proceedings
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Volume:
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2788
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Page range:
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181-186
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Conference title:
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OM 2020
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Location of the conference venue:
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Online
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Date of the conference:
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02.11.2020
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Publisher:
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Shvaiko, Pavel
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Euzenat, Jérôme
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Jiménez-Ruiz, Ernesto
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Hassanzadeh, Oktie
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Trojahn, Cássia
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Place of publication:
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Aachen, Germany
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Publishing house:
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RWTH Aachen
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ISSN:
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1613-0073
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
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Pre-existing license:
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 Creative Commons Attribution 4.0 International (CC BY 4.0)
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Subject:
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004 Computer science, internet
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Individual keywords (German):
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Datenintegration
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Keywords (English):
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ontology matching , data integration , semantic matching
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Abstract:
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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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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Search Authors in
BASE:
Monych, Michael
;
Portisch, Jan
;
Hladik, Michael
;
Paulheim, Heiko
Google Scholar:
Monych, Michael
;
Portisch, Jan
;
Hladik, Michael
;
Paulheim, Heiko
ORCID:
Monych, Michael, Portisch, Jan  ORCID: https://orcid.org/0000-0001-5420-0663, Hladik, Michael and Paulheim, Heiko ORCID: https://orcid.org/0000-0003-4386-8195
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