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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
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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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Shuai, S., Hu, Z., Zhang, B., Liaqat, H. B., & Kong, X. (2023) · Information · doi:10.3390/info14120647 · Cited by 10
Knowledge Graph for Power Grid Dispatching of Digital Homes based on Graph Convolutional Network
Peng, F., An, T., Li, D., Wang, H., Tian, C., & Chen, Z. (2020) · International Conference on Digital Home (ICDH) · doi:10.1109/icdh51081.2020.00042 · Cited by 9
DensE: An Enhanced Non-Abelian Group Representation for Knowledge Graph Embedding.Preprint
Lu, H., & Hu, H. (2020) · arXiv (Cornell University) · Cited by 9
Leveraging semantic property for temporal knowledge graph completion
Li, M., Sun, Z., Zhang, W., & Liu, W. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03981-8 · Cited by 8
Stream2Graph: Dynamic Knowledge Graph for Online Learning Applied in Large-scale Network
Barry, M., Bifet, A., Chiky, R., Jaouhari, S. E., Montiel, J., Ouafi, A. E., & Guerizec, E. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020885 · Cited by 8
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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