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Machine Learning for Refining Knowledge Graphs: A Survey

Subagdja, Budhitama, Shanthoshigaa, D., Wang, Zhaoxia, Tan, Ah-Hwee·2024·ACM Computing Surveys
DOI 10.1145/3640313
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Abstract

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in KGs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this article presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into the development of fully automated KG refinement.

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Metadata

Authors
Subagdja, B., Shanthoshigaa, D., Wang, Z., & Tan, A.-H.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2024-02-23
Volume
56
Issue
6
Pages
1-38
ISSN
  • 0360-0300
  • 1557-7341
DOI
10.1145/3640313
Cited by
24
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • A*STAR under its Advanced Manufacturing and Engineering (AME) Programmatic Grant (A19E2b0098)
  • Jubilee Technology Fellowship awarded to Ah-Hwee Tan by Singapore Management University

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