research-article

A comprehensive overview of knowledge graph completion

Authors:

Jingwei ChengAuthors Info & Claims

Volume 255, Issue C

https://doi.org/10.1016/j.knosys.2022.109597

Published: 14 November 2022 Publication History

Abstract

Knowledge Graph (KG) provides high-quality structured knowledge for various downstream knowledge-aware tasks (such as recommendation and intelligent question-answering) with its unique advantages of representing and managing massive knowledge. The quality and completeness of KGs largely determine the effectiveness of the downstream tasks. But in view of the incomplete characteristics of KGs, there is still a large amount of valuable knowledge is missing from the KGs. Therefore, it is necessary to improve the existing KGs to supplement the missed knowledge. Knowledge Graph Completion (KGC) is one of the popular technologies for knowledge supplement. Accordingly, there has a growing concern over the KGC technologies. Recently, there have been lots of studies focusing on the KGC field. To investigate and serve as a helpful resource for researchers to grasp the main ideas and results of KGC studies, and further highlight ongoing research in KGC, in this paper, we provide a all-round up-to-date overview of the current state-of-the-art in KGC.

According to the information sources used in KGC methods, we divide the existing KGC methods into two main categories: the KGC methods relying on structural information and the KGC methods using other additional information. Further, each category is subdivided into different granularity for summarizing and comparing them. Besides, the other KGC methods for KGs of special fields (including temporal KGC, commonsense KGC, and hyper-relational KGC) are also introduced. In particular, we discuss comparisons and analyses for each category in our overview. Finally, some discussions and directions for future research are provided.

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Zan SJi WZhou G(2025)Knowledge graph embeddings based on 2d convolution and self-attention mechanisms for link predictionApplied Intelligence10.1007/s10489-024-05977-y55:2Online publication date: 1-Jan-2025
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Le DZhong SLiu ZXu SChaudhary VZhou KXu ZSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Knowledge graphs can be learned with just intersection featuresProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693117(26199-26214)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693117
Korn DHou PSchatz KBeasley JTropsha AChirkova R(2024)Towards Improving the Efficiency of Drug Repurposing by Leveraging Node Promiscuity in Biomedical Knowledge GraphsACM Transactions on Computing for Healthcare10.1145/37076506:1(1-32)Online publication date: 9-Dec-2024
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Index Terms

A comprehensive overview of knowledge graph completion
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 255, Issue C

Nov 2022

756 pages

ISSN:0950-7051

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 14 November 2022

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Zan SJi WZhou G(2025)Knowledge graph embeddings based on 2d convolution and self-attention mechanisms for link predictionApplied Intelligence10.1007/s10489-024-05977-y55:2Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-05977-y
Le DZhong SLiu ZXu SChaudhary VZhou KXu ZSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Knowledge graphs can be learned with just intersection featuresProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693117(26199-26214)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693117
Korn DHou PSchatz KBeasley JTropsha AChirkova R(2024)Towards Improving the Efficiency of Drug Repurposing by Leveraging Node Promiscuity in Biomedical Knowledge GraphsACM Transactions on Computing for Healthcare10.1145/37076506:1(1-32)Online publication date: 9-Dec-2024
https://dl.acm.org/doi/10.1145/3707650
Zu LLin LFu SGuo FWu J(2024)SelectEKnowledge-Based Systems10.1016/j.knosys.2024.111554291:COnline publication date: 12-May-2024
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Long MChen SWang J(2024)Locally-adaptive mapping for network alignment via meta-learningInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10381761:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103817
Zhang FQiu PShen TCheng JLi W(2024)gMLP-KGE: a simple but efficient MLPs with gating architecture for link predictionApplied Intelligence10.1007/s10489-024-05677-754:20(9594-9606)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10489-024-05677-7
Song YGui MZhang KXu ZDai DKong D(2024)Relational multi-scale metric learning for few-shot knowledge graph completionKnowledge and Information Systems10.1007/s10115-024-02083-w66:7(4125-4150)Online publication date: 1-Jul-2024
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Sun XChen QHao MLi YSun B(2024)Mconvkgc: a novel multi-channel convolutional model for knowledge graph completionComputing10.1007/s00607-023-01247-w106:3(915-937)Online publication date: 1-Mar-2024
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Wang ZShao YPeng BLi BLi YWang QLi N(2024)Multi-view Transformer-Based Network for Prerequisite Learning in Concept GraphsThe Semantic Web – ISWC 202410.1007/978-3-031-77844-5_4(67-86)Online publication date: 11-Nov-2024
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Le TTran HLe B(2023)Knowledge graph embedding with the special orthogonal group in quaternion space for link predictionKnowledge-Based Systems10.1016/j.knosys.2023.110400266:COnline publication date: 22-Apr-2023
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