Cited by 2,947Advanced Graph Neural NetworksTopic ModelingSemantic Web and Ontologies

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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

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Ji, Shaoxiong, Pan, Shirui, Cambria, Erik, Marttinen, Pekka, Yu, Philip S.·2022·IEEE Transactions on Neural Networks and Learning Systems

DOI 10.1109/tnnls.2021.3070843

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Abstract

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

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Authors: Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S.
Affiliations: Aalto University (FI)
Monash University (AU)
Nanyang Technological University (SG)
University of Illinois Chicago (US)
ORCID: Ji, Shaoxiong
Pan, Shirui
Cambria, Erik
Marttinen, Pekka
Yu, Philip S.
Venue: IEEE Transactions on Neural Networks and Learning Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-02
Volume: 33
Issue: 2
Pages: 494-514
ISSN: 2162-237X (ISSN-L)
2162-2388
DOI: 10.1109/tnnls.2021.3070843
OpenAlex: W3003265726
Cited by: 2,947
References: 272
Citations by year: 2026: 514 · 2025: 623 · 2024: 588 · 2023: 566 · 2022: 364
FWCI: 153.72
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: other-oa
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
PubMed (publishedVersion)
Aaltodoc (Aalto University) (submittedVersion, other-oa)
arXiv (Cornell University) (submittedVersion)
DR-NTU (Nanyang Technological University) (submittedVersion)
Griffith Research Online (submittedVersion)
arXiv (Cornell University)
Funders: NSF (III-1763325, III-1909323, SaTC-1930941)
Agency for Science, Technology and Research (A*STAR) through its AME Programmatic Funding Scheme (A18A2b0046)
Academy of Finland (336033, 315896)
Business Finland (884/31/2018)
EU H2020 (101016775)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords: knowledge graphs, knowledge graph embedding, knowledge acquisition, knowledge graph completion, temporal knowledge graphs, commonsense reasoning

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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

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