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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Open accessAbstract
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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“Ji et al. [31] divided the research on KGs into four categories: knowledge representation learning, knowledge acquisition, temporal KGs and knowledge-aware applications.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [31]
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Anand, A., Gupta, M., Prasad, K., Goel, U., Lal, N., Verma, A., & Shah, R. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49601-1_3 · Cited by 8
Wasserstein adversarial learning based temporal knowledge graph embedding
Dai, Y., Guo, W., & Eickhoff, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.120061 · Cited by 8
Zhao, Y., Li, K., Wang, T., & Zhang, S. (2024) · Soft Computing · doi:10.1007/s00500-024-09629-8 · Cited by 7
Joint Multi-Facts Reasoning Network for Complex Temporal Question Answering Over Knowledge Graph
Huang, R., Wei, W., Qu, X., Xie, W., Mao, X.-L., & Chen, D. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447439 · Cited by 11
Metadata
- 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)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2022-02
- Volume
- 33
- Issue
- 2
- Pages
- 494-514
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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