Machine learning for refining knowledge graphs: A survey

• Main Authors: SUBAGDJA, Budhitama, Shanthoshigaa, D., WANG, Zhaoxia, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: knowledge graphs; knowledge graph refinement; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8552; https://ink.library.smu.edu.sg/context/sis_research/article/9555/viewcontent/3640313_pvoa_cc_by.pdf
• Institution: Singapore Management University

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.