Order matters: Matching multiple knowledge graphs

Hertling, Sven ; Paulheim, Heiko

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DOI: https://doi.org/10.1145/3460210.3493556
URL: https://madoc.bib.uni-mannheim.de/61042
Additional URL: https://dl.acm.org/doi/10.1145/3460210.3493556
URN: urn:nbn:de:bsz:180-madoc-610423
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: K-CAP '21: Proceedings of the 11th on Knowledge Capture Conference
Page range: 113-120
Conference title: K-CAP '21
Location of the conference venue: Online
Date of the conference: 02.-03.12.2021
Publisher: Gentile, Anna Lisa
Place of publication: New York, NY
Publishing house: Association for Computing Machinery
ISBN: 978-1-4503-8457-5
Related URLs:
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: Knowledge graphs (KGs) provide information in machine interpretable form. In cases where multiple KGs are used in the same system, that information needs to be integrated. This is usually done by automated matching systems. Most of those systems consider only 1:1 (binary) matching tasks. Thus, matching a larger number of knowledge graphs with such systems would lead to quadratic efforts. In this paper, we empirically analyze different approaches to reduce the task of multi-source matching to a linear number of executions of binary matching systems. We show that the matching order of KGs and the multi-source strategy actually matter and that near-optimal results can be achieved with linear efforts.

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