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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Items 201–267 of 267
Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis
Cambria, E., Song, Y., Wang, H., & Howard, N. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2012.118 · Cited by 106
Self-citationProbabilistic Reasoning via Deep Learning: Neural Association ModelsPreprint
Liu, Q., Jiang, H., Evdokimov, A., Ling, Z.-H., Zhu, X., Wei, S., & Hu, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07704 · Cited by 105
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree SearchPreprint
Shen, Y., Chen, J., Huang, P.-S., Guo, Y., & Gao, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04394 · Cited by 104
Leveraging Deep Neural Networks and Knowledge Graphs for Entity DisambiguationPreprint
Huang, H., Heck, L. P., & Ji, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07678 · Cited by 103
Efficient Probabilistic Logic Reasoning with Graph Neural NetworksPreprint
Zhang, Y., Chen, X., Yang, Y., Ramamurthy, A., Li, B., Qi, Y., & Song, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11850 · Cited by 102
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models
He, B., Zhou, D., Xiao, J., Jiang, X., Liu, Q., Yuan, N. J., & Xu, T. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.207 · Cited by 102
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks.
Jiang, X., Wang, Q., Li, P., & Wang, B. (2016) · International Conference on Computational Linguistics · Cited by 100
Incorporating Relation Paths in Neural Relation Extraction
Zeng, W., Lin, Y., Liu, Z., & Sun, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1186 · Cited by 100
Large Scaled Relation Extraction With Reinforcement Learning
Zeng, X., He, S., Liu, K., & Zhao, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11950 · Cited by 99
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
Lv, X., Gu, Y., Han, X., Hou, L., Li, J., & Liu, Z. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1334 · Cited by 97
Knowledge graph embedding by flexible translation
Feng, J., Huang, M., Wang, M., Zhou, M., Hao, Y., & Zhu, X. (2016) · Principles of Knowledge Representation and Reasoning · Cited by 96
Knowledge Graph Embedding with Hierarchical Relation Structure
Zhang, Z., Zhuang, F., Qu, M., Lin, F., & He, Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1358 · Cited by 89
Few-shot Relation Extraction via Bayesian Meta-learning on Relation GraphsPreprint
Qu, M., Gao, T., Xhonneux, L.-P., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02387 · Cited by 88
Knowledge representation learning with entities, attributes and relations
Lin, Y., Liu, Z., & Sun, M. (2016) · International Joint Conference on Artificial Intelligence · Cited by 87
Entity Disambiguation by Knowledge and Text Jointly Embedding
Fang, W., Zhang, J., Wang, D., Chen, Z., & Li, M. (2016) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1026 · Cited by 83
Dialog-to-action: conversational question answering over a large-scale knowledge base
Guo, D., Tang, D., Duan, N., Zhou, M., & Yin, J. P. (2018) · neural information processing systems · Cited by 81
Attribute Propagation Network for Graph Zero-Shot Learning
Liu, L., Zhou, T., Long, G., Jiang, J. B., & Chengqi Zhang, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5923 · Cited by 81
Relation Embedding with Dihedral Group in Knowledge Graph
Xu, C., & Li, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1026 · Cited by 76
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Qin, P., Wang, X. E., Chen, W., Zhang, C., Xu, W., & Wang, W. Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6392 · Cited by 75
TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics
Yang, S., Tian, J., Zhang, H., Yan, J., He, H., & Jin, Y. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/268 · Cited by 75
An Embedding-Based Approach to Rule Learning in Knowledge Graphs
Omran, P. G., Wang, K., & Wang, Z. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2941685 · Cited by 74
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162876 · Cited by 74
On the Equivalence of Holographic and Complex Embeddings for Link Prediction
Hayashi, K., & Shimbo, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2088 · Cited by 73
Preprogramming for mechanical translation.
Richens, R. H. (1956) · Mech. Transl. Comput. Linguistics · Cited by 69
A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications
Wu, T., Qi, G., Li, C., & Wang, M. (2018) · Sustainability · doi:10.3390/su10093245 · Cited by 67
Connecting Embeddings for Knowledge Graph Entity Typing
Zhao, Y., zhang, A., Xie, R., Liu, K., & Wang, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.572 · Cited by 66
On Multi-Relational Link Prediction with Bilinear Models
Wang, Y., Gemulla, R., & Li, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11738 · Cited by 65
Knowledge Representation Learning: A Quantitative ReviewPreprint
Lin, Y., Han, X., Xie, R., Liu, Z., & Sun, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.10901 · Cited by 63
Collaborative Policy Learning for Open Knowledge Graph Reasoning
Fu, C., Chen, T., Qu, M., Jin, W., & Ren, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1269 · Cited by 63
Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism
Qian, W. P., Fu, C., Zhu, Y., Cai, D., & He, X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/596 · Cited by 63
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
Shen, T., Mao, Y., He, P., Long, G., Trischler, A., & Chen, W. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.722 · Cited by 63
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Liu, T., Zhang, X., Zhou, W., & Jia, W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1243 · Cited by 59
Knowledge Questions from Knowledge Graphs
Seyler, D., Yahya, M., & Berberich, K. (2017) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3121050.3121073 · Cited by 57
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base
Hao, B., Zhu, H., & Paschalidis, I. C. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.57 · Cited by 57
Effective Blending of Two and Three-way Interactions for Modeling Multi-relational Data
García-Durán, A., Bordes, A., & Usunier, N. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44848-9_28 · Cited by 55
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionPreprint
Baek, J., Lee, D. B., & Hwang, S. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06648 · Cited by 51
Integrating Graph Contextualized Knowledge into Pre-trained Language ModelsPreprint
He, B., Zhou, D., Xiao, J., Jiang, X., Liu, Q., Yuan, N. J., & Xu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00147 · Cited by 47
Open Knowledge Enrichment for Long-tail Entities
Cao, E., Wang, D., Huang, J., & Hu, W. P. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380123 · Cited by 43
Structuring Knowledge in a Graph
Stokman, F. N., & de Vries, P. H. (1988) · Springer eBooks · doi:10.1007/978-3-642-73402-1_12 · Cited by 42
Evolving knowledge graph similarity for supervised learning in complex biomedical domains
Sousa, R. T., Silva, S., & Pesquita, C. (2020) · BMC Bioinformatics · doi:10.1186/s12859-019-3296-1 · Cited by 42
Towards Understanding the Geometry of Knowledge Graph Embeddings
Chandrahas, Sharma, A. J., & Talukdar, P. P. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1012 · Cited by 40
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction
Safavi, T., Koutra, D., & Meij, E. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.667 · Cited by 38
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Ye, H., Chao, W., Luo, Z., & Li, Z. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1166 · Cited by 37
Context-Aware Temporal Knowledge Graph Embedding
Liu, Y., Hua, W., Xin, K., & Zhou, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34223-4_37 · Cited by 36
Cooperative Denoising for Distantly Supervised Relation Extraction
Lei, K., Chen, D., Li, Y., Du, N., Yang, M., Fan, W., & Shen, Y. (2018) · International Conference on Computational Linguistics · Cited by 34
Recurrent Event Network for Reasoning over Temporal Knowledge Graphs.Preprint
Jin, W., Zhang, C., Szekely, P. A., & Ren, X. (2019) · arXiv (Cornell University) · Cited by 31
Shared Embedding Based Neural Networks for Knowledge Graph Completion
Guan, S., Jin, X., Wang, Y., & Cheng, X. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271704 · Cited by 30
Triple Classification Using Regions and Fine-Grained Entity Typing
Dong, T., Wang, Z., Li, J., Bauckhage, C., & Cremers, A. B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.330177 · Cited by 30
Leveraging Multi-Token Entities in Document-Level Named Entity Recognition
Hu, A., Dou, Z., Nie, J., & Wen, J.-R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6304 · Cited by 27
LowFER: Low-rank Bilinear Pooling for Link PredictionPreprint
Amin, S., Varanasi, S., Dunfield, K. A., & Neumann, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10858 · Cited by 27
Differentiable learning of numerical rules in knowledge graphs
Wang, P.-W., Stepanova, D., Domokos, C., & Kolter, J. Z. (2020) · International Conference on Learning Representations · Cited by 26
Chen, Y.-N., Wang, W. Y., & Rudnicky, A. I. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1064 · Cited by 25
Marrying Uncertainty and Time in Knowledge Graphs
Chekol, M., Pirrò, G., Schoenfisch, J., & Stuckenschmidt, H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10495 · Cited by 24
Probability Calibration for Knowledge Graph Embedding ModelsPreprint
Tabacof, P., & Costabello, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10000 · Cited by 22
Expanding Holographic Embeddings for Knowledge Completion
Xue, Y., Yang, Y., Xu, Z., & Sabharwal, A. (2018) · neural information processing systems · Cited by 22
Embedding Models for Episodic Knowledge GraphsPreprint
Ma, Y., Tresp, V., & Daxberger, E. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3319790 · Cited by 20
On the Generative Discovery of Structured Medical Knowledge
Zhang, C., Li, Y., Du, N., Fan, W., & Yu, P. S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220010 · Cited by 16
Self-citationFew-shot Relation Extraction via Bayesian Meta-learning on Task Graphs
Qu, M., Gao, T., Xhonneux, L.-P., & Tang, J. (2020) · International Conference on Machine Learning · Cited by 16
Relation Extraction with Explanation
Shahbazi, H., Fern, X. Z., Ghaeini, R., & Tadepalli, P. V. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.579 · Cited by 14
CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection
Wijaya, D. T., Nakashole, N., & Mitchell, T. M. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1207 · Cited by 13
Discovering Correlations between Sparse Features in Distant Supervision for Relation Extraction
Qu, J., Ouyang, D., Hua, W., Ye, Y., & Zhou, X. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291004 · Cited by 11
Relation Extraction Using Supervision from Topic Knowledge of Relation Labels
Jiang, H., Cui, L., Xu, Z., Yang, D., Chen, J., Li, C., Liu, J., Liang, J., Wang, C., Xiao, Y., et al. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/698 · Cited by 11
From One Point to A Manifold: Orbit Models for Knowledge Graph Embedding.Preprint
Xiao, H., Huang, M., Hao, Y., & Zhu, X. (2015) · arXiv (Cornell University) · Cited by 8
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017) · arXiv · doi:10.48550/arxiv.1705.05742
Tensor Decompositions for temporal knowledge base completion
Lacroix, T., Obozinski, G., & Usunier, N. (2020) · arXiv · doi:10.48550/arxiv.2004.04926
Research works printed but not linked: 2
- Zhang, Quaternion knowledge graph embedding, 2731
- 10.18653/v1/P19-1466 · DOI
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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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