Publication Type
Journal Article
Version
publishedVersion
Publication Date
2-2024
Abstract
Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. 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 paper 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.
Keywords
knowledge graphs, knowledge graph refinement
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
ACM Computing Surveys
Volume
56
Issue
6
First Page
1
Last Page
38
ISSN
0360-0300
Identifier
10.1145/3640313
Publisher
Association for Computing Machinery (ACM)
Citation
SUBAGDJA, Budhitama; Shanthoshigaa, D.; WANG, Zhaoxia; and TAN, Ah-hwee.
Machine learning for refining knowledge graphs: A survey. (2024). ACM Computing Surveys. 56, (6), 1-38.
Available at: https://ink.library.smu.edu.sg/sis_research/8552
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3640313