The DOI matches its Crossref record; 267 of the 272 references it cites were found in a registry or an index.
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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Research works printed but not linked: 2
- Zhang, Quaternion knowledge graph embedding, 2731
- 10.18653/v1/P19-1466 · DOI
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Text Data Augmentation for Deep Learning
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,852
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123618 · Cited by 1,452
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
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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
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