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FinMatcher at FinSim-2: hypernym detection in the financial services domain using knowledge graphs


Portisch, Jan ; Hladik, Michael ; Paulheim, Heiko


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DOI: https://doi.org/10.1145/3442442.3451382
URL: https://madoc.bib.uni-mannheim.de/60289
Additional URL: https://arxiv.org/abs/2103.01576
URN: urn:nbn:de:bsz:180-madoc-602890
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: Companion Proceedings of the Web Conference 2021
Page range: 293-297
Conference title: WWW '21
Location of the conference venue: Online
Date of the conference: 12.-23.04.2021
Publisher: Leskovec, Jure ; Grobelnik, Marko ; Najork, Marc ; Tang, Jie ; Zia, Leila
Place of publication: New York, NY, USA
Publishing house: Association for Computing Machinery
ISBN: 978-1-4503-8313-4
Publication language: English
Institution: School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference. The FinSim-2 shared task consists of a set of concept labels from the financial services domain. The goal is to find the most relevant top-level concept from a given set of concepts. The FinMatcher system exploits three publicly available knowledge graphs, namely WordNet, Wikidata, and WebIsALOD. The graphs are used to generate explicit features as well as latent features which are fed into a neural classifier to predict the closest hypernym.




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BASE: Portisch, Jan ; Hladik, Michael ; Paulheim, Heiko

Google Scholar: Portisch, Jan ; Hladik, Michael ; Paulheim, Heiko

ORCID: Portisch, Jan ORCID: 0000-0001-5420-0663 ; Hladik, Michael ; Paulheim, Heiko ORCID: 0000-0003-4386-8195

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