Fine-TOM matcher results for OAEI 2021


Knorr, Leon ; Portisch, Jan


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URL: https://madoc.bib.uni-mannheim.de/61198
Additional URL: http://ceur-ws.org/Vol-3063/
URN: urn:nbn:de:bsz:180-madoc-611989
Document Type: Conference or workshop publication
Year of publication: 2022
Book title: OM 2021, Ontology Matching 2021 : Proceedings of the 16th International Workshop on Ontology Matching, co-located with the 20th International Semantic Web Conference (ISWC 2021), virtual conference, October 25, 2021
The title of a journal, publication series: CEUR Workshop Proceedings
Volume: 3063
Page range: 144-151
Conference title: OM 2021
Location of the conference venue: Online
Date of the conference: 25.10.2021
Publisher: Shvaiko, Pavel ; Euzenat, Jérôme ; Jiménez-Ruiz, Ernesto ; Hassanzadeh, Oktie ; Trojahn, Cássia
Place of publication: Aachen, Germany
Publishing house: RWTH Aachen
ISSN: 1613-0073
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 , Semantische Datenintegration
Keywords (English): ontology matching , ontology alignment , language models , transformers , fine-tuning , data integration , semantic data integration
Abstract: In this paper, the Fine-Tuned Transformes for Ontology matching (Fine-TOM) matching system is presented along with the results it achieved during its first participation in the Ontology Alignment Evaluation Initiative (OAEI) campaign (2021). The system uses the publicly available albert-base-v2 model, which has been fine-tuned with a training dataset that includes 20% of each reference alignment from the Anatomy, Conference, and Knowledge Graph track, as well as a wide variety of generated false examples. The model is then used by a separate matching pipeline which calculates a confidence score for each correspondence. In the submitted docker container, only the matching pipeline with an already fine-tuned model is included.
Additional information: Online-Ressource

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ORCID: Knorr, Leon ; Portisch, Jan ORCID: 0000-0001-5420-0663

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