Cross-knowledge-graph entity alignment via relation prediction

Huang, H., Li, C., Peng, X. orcid.org/0000-0001-5787-9982 et al. (5 more authors) (2022) Cross-knowledge-graph entity alignment via relation prediction. Knowledge-Based Systems, 240. 107813. ISSN 0950-7051

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Elsevier B.V. This is an author produced version of a paper subsequently published in Knowledge-Based Systems. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Knowledge alignment; Anchor relation; Self-training; Data augmentation; Relation prediction
Dates:
  • Accepted: 23 November 2021
  • Published (online): 17 December 2021
  • Published: 15 March 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 20 Dec 2021 12:55
Last Modified: 15 Mar 2022 13:04
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.knosys.2021.107813

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Filename: Cross-knowledge-graph entity alignment via relation prediction.pdf

Licence: CC-BY-NC-ND 4.0

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