Skip to main content
SpringerLink
Log in

A survey of inductive knowledge graph completion

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Knowledge graph completion (KGC) can enhance the completeness of the knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations employed during testing have been seen in the training phase. As real-world KGs constantly evolve, it becomes imperative to retrain the KG whenever unseen entities or relations appear. Inductive knowledge graph completion (IKGC) aims to complete missing triples involving unseen entities or relations without retraining and has garnered substantial attention recently. This paper is the first one that provides a comprehensive review of IKGC from both technical and theoretical perspectives. Technically, IKGC is categorized into two groups: structural information-based IKGC and additional information-based IKGC. Theoretically, IKGC is divided into two categories: semi-inductive setting and fully-inductive setting. In particular, each category is further subdivided into distinct granularities, and theoretical scenarios are incorporated into the technical methods to facilitate comparison and analysis. Finally, future research directions are prospected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Huang X, Zhang J, Li D, et al (2019) Knowledge graph embedding based question answering. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 105–113

  2. Wang X, Wang D, Xu C, et al (2019) Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the 33th AAAI Conference on Artificial Intelligence, pp 5329–5336

  3. Jia Y, Qi Y, Shang H et al (2018) A practical approach to constructing a knowledge graph for cybersecurity. Engineering 4(1):53–60. https://doi.org/10.1016/j.eng.2018.01.004

    Article  Google Scholar 

  4. Krompaß D, Baier S, Tresp V (2015) Type-constrained representation learning in knowledge graphs. In: Proceedings of 14th International Semantic Web Conference, pp 640–655

  5. Dong X, Gabrilovich E, Heitz G, et al (2014) Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 601–610

  6. Xie R, Liu Z, Jia J, et al (2016) Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp 2659–2665

  7. Shi B, Weninger T (2018) Open-world knowledge graph completion. In: Proceedings of 32nd AAAI conference on artificial intelligence, pp 1957–1964

  8. Niu L, Fu C, Yang Q et al (2021) Open-world knowledge graph completion with multiple interaction attention. World Wide Web 24:419–439. https://doi.org/10.1007/s11280-020-00847-2

    Article  Google Scholar 

  9. Zhang C, Yao H, Huang C, et al (2020) Few-shot knowledge graph completion. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 3041–3048

  10. Sheng J, Guo S, Chen Z, et al (2020) Adaptive attentional network for few-shot knowledge graph completion. arXiv preprint https://doi.org/10.48550/arXiv.2010.09638

  11. Wang S, Huang X, Chen C, et al (2021) Reform: Error-aware few-shot knowledge graph completion. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp 1979–1988

  12. Hamaguchi T, Oiwa H, Shimbo M, et al (2017) Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 1802–1808

  13. Bi Z, Zhang T, Zhou P et al (2020) Knowledge transfer for out-of-knowledge-base entities: improving graph-neural-network-based embedding using convolutional layers. IEEE Access 8:159039–159049

    Article  Google Scholar 

  14. Zhao M, Jia W, Huang Y (2020) Attention-based aggregation graph networks for knowledge graph information transfer. In: Proceedings of the 24th Pacific-Asia conference of advances in knowledge discovery and data mining, pp 542–554

  15. Wang C, Zhou X, Pan S, et al (2022) Exploring relational semantics for inductive knowledge graph completion. In: Proceedings of the 36th AAAI conference on artificial intelligence, pp 4184–4192

  16. Ali M, Berrendorf M, Galkin M, et al (2021) Improving inductive link prediction using hyper-relational facts. In: Proceedings of the 20th international semantic web conference, pp 74–92

  17. Gesese GA, Sack H, Alam M (2022) Raild: Towards leveraging relation features for inductive link prediction in knowledge graphs. arXiv preprint https://doi.org/10.48550/arXiv.2211.11407

  18. Liang K, Meng L, Liu M, et al (2023) A survey of knowledge graph reasoning on graph types: Static, dynamic, and multimodal. arXiv preprint https://doi.org/10.48550/arXiv.2212.05767

  19. Woods WA (1975) What’s in a link: Foundations for semantic networks. Representation and understanding pp 35–82

  20. Guarino N (1995) Formal ontology, conceptual analysis and knowledge representation. Int J Hum Comput Stud 43(5–6):625–640

    Article  Google Scholar 

  21. Berners-Lee T, Cailliau R, Groff JF et al (1992) World-wide web: the information universe. Internet Res 2(1):52–58

    Article  Google Scholar 

  22. Davies J, Fensel D, Van Harmelen F (2003) Towards the semantic web. Ontology-driven knowledge management

  23. Fensel D, Şimşek U, Angele K et al (2020) Introduction: what is a knowledge graph? Methodology, tools and selected use cases, Knowledge graphs, pp 1–10

  24. Wang Z, Zhang J, Feng J, et al (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI conference on artificial intelligence, pp 1112–1119

  25. Lin Y, Liu Z, Sun M, et al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp 2181-2187

  26. Nickel M, Tresp V, Kriegel HP, et al (2011) A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th international conference on machine learning (ICML), pp 3104482–3104584

  27. Dettmers T, Minervini P, Stenetorp P, et al (2018) Convolutional 2d knowledge graph embeddings. In: Proceedings of the 32nd AAAI conference on artificial intelligence, pp 1811–1818

  28. Schlichtkrull M, Kipf TN, Bloem P, et al (2018) Modeling relational data with graph convolutional networks. In: Proceedings of the 15th international semantic web conference, pp 593–607

  29. Galárraga LA, Teflioudi C, Hose K, et al (2013) Amie: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd international conference on World Wide Web, pp 413–422

  30. Galárraga L, Teflioudi C, Hose K et al (2015) Fast rule mining in ontological knowledge bases with amie+. VLDB J 24(6):707–730

    Article  Google Scholar 

  31. Meilicke C, Fink M, Wang Y, et al (2018) Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion. In: Proceedings of the 17th international semantic web conference, pp 3–20

  32. Baek J, Lee DB, Hwang SJ (2020) Learning to extrapolate knowledge: transductive few-shot out-of-graph link prediction. Adv Neural Inf Process Syst 33:546–560

    Google Scholar 

  33. Zhang Y, Wang W, Chen W, et al (2021) Meta-learning based hyper-relation feature modeling for out-of-knowledge-base embedding. In: Proceedings of the 30th ACM international conference on information and knowledge management, pp 2637–2646

  34. Chen M, Zhang W, Zhu Y, et al (2022a) Meta-knowledge transfer for inductive knowledge graph embedding. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 927–937

  35. Chen M, Zhang W, Yao Z, et al (2022b) Meta-learning based knowledge extrapolation for knowledge graphs in the federated setting. arXiv preprint https://doi.org/10.48550/arXiv.2205.04692

  36. Teru K, Denis E, Hamilton W (2020) Inductive relation prediction by subgraph reasoning. In: Proceedings of the 37th international conference on machine learning, pp 9448–9457

  37. Chen J, He H, Wu F, et al (2021) Topology-aware correlations between relations for inductive link prediction in knowledge graphs. In: Proceedings of the 35th AAAI conference on artificial intelligence, pp 6271–6278

  38. Mai S, Zheng S, Yang Y, et al (2021) Communicative message passing for inductive relation reasoning. In: Proceedings of the 35th AAAI conference on artificial intelligence, pp 4294–4302

  39. Yao L, Mao C, Luo Y (2019) Kg-bert: Bert for knowledge graph completion. arXiv preprint https://doi.org/10.48550/arXiv.1909.03193

  40. Kim B, Hong T, Ko Y, et al (2020) Multi-task learning for knowledge graph completion with pre-trained language models. In: Proceedings of the 28th international conference on computational linguistics, pp 1737–1743

  41. Geng Y, Chen J, Zhang W, et al (2022) Disentangled ontology embedding for zero-shot learning. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 443–453

  42. Wang J, Wang X, Luo X, et al (2020) Open-world relationship prediction. In: Proceedings of the 32nd international conference on tools with artificial intelligence (ICTAI), pp 323–330

  43. Wang P, Han J, Li C, et al (2019) Logic attention based neighborhood aggregation for inductive knowledge graph embedding. In: Proceedings of the 33th AAAI conference on artificial intelligence, pp 7152–7159

  44. Li M, Sun Z, Zhang W (2022) Slan: similarity-aware aggregation network for embedding out-of-knowledge-graph entities. Neurocomputing 491:186–196. https://doi.org/10.1016/j.neucom.2022.03.063

    Article  Google Scholar 

  45. Ren C, Zhang L, Fang L, et al (2021) Ontological concept structure aware knowledge transfer for inductive knowledge graph embedding. In: Proceedings of the 2021 international joint conference on neural networks (IJCNN), pp 1–8

  46. Zhong S, Yue K, Duan L (2022) Attention-based relation prediction of knowledge graph by incorporating graph and context features. In: Proceedings of the 2022 conference on web information systems engineering, pp 259–273

  47. Li M, Sun Z, Zhang W (2019) Open knowledge graph representation learning based on neighbors and semantic affinity. J Comput Res Dev 56(12):2549–2561 (in Chinese)

    ADS  Google Scholar 

  48. Albooyeh M, Goel R, Kazemi SM (2020) Out-of-sample representation learning for knowledge graphs. In: Proceedings of the 2020 conference on empirical methods in natural language processing, pp 2657–2666

  49. Dai D, Zheng H, Luo F, et al (2020) Inductively representing out-of-knowledge-graph entities by optimal estimation under translational assumptions. arXiv preprint https://doi.org/10.48550/arXiv.2009.12765

  50. Cui Y, Wang Y, Sun Z, et al (2022) Lifelong embedding learning and transfer for growing knowledge graphs. arXiv preprint https://doi.org/10.48550/arXiv.2211.15845

  51. Meilicke C, Chekol MW, Ruffinelli D, et al (2019) Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 3137–3143

  52. Yang F, Yang Z, Cohen WW (2017) Differentiable learning of logical rules for knowledge base reasoning. In: Proceedings of the 31st international conference on neural information processing systems, pp 2316–2325

  53. Sadeghian A, Armandpour M, Ding P, et al (2019) Drum: end-to-end differentiable rule mining on knowledge graphs. In: Proceedings of the 33rd international conference on neural information processing systems, pp 15347–15357

  54. Qu M, Chen J, Xhonneux LP, et al (2020) Rnnlogic: learning logic rules for reasoning on knowledge graphs. https://doi.org/10.48550/arXiv.2010.04029

  55. Zhang Y, Li Y, Zhang Y et al (2023) Missing-edge aware knowledge graph inductive inference through dual graph learning and traversing. Expert Syst Appl 213:118969

    Article  Google Scholar 

  56. Zhu Z, Zhang Z, Xhonneux LP et al (2021) Neural bellman-ford networks: a general graph neural network framework for link prediction. Adv Neural Inf Process Syst 34:29476–29490

    Google Scholar 

  57. Liu S, Grau B, Horrocks I et al (2021) Indigo: GNN-based inductive knowledge graph completion using pair-wise encoding. Adv Neural Inf Process Syst 34:2034–2045

    Google Scholar 

  58. Yan Z, Ma T, Gao L, et al (2022) Cycle representation learning for inductive relation prediction. In: Proceedings of the 39th international conference on machine learning, pp 24895–24910

  59. Zhang Y, Yao Q (2022) Knowledge graph reasoning with relational digraph. Proc ACM Web Conf 2022:912–924

    Google Scholar 

  60. Pan Y, Liu J, Zhang L, et al (2021) Learning first-order rules with relational path contrast for inductive relation reasoning. arXiv preprint https://doi.org/10.48550/arXiv.2110.08810

  61. Mai S, Zheng S, Sun Y et al (2022) Dynamic graph dropout for subgraph-based relation prediction. Knowl-Based Syst 250:109172

    Article  Google Scholar 

  62. Kwak H, Jung H, Bae K (2022) Subgraph representation learning with hard negative samples for inductive link prediction. In: Proceedings of the 2022 international conference on acoustics, speech and signal processing (ICASSP), pp 4768–4772

  63. Zheng S, Mai S, Sun Y et al (2022) Subgraph-aware few-shot inductive link prediction via meta-learning. IEEE Trans Knowl Data Eng 35(6):6512–6517. https://doi.org/10.1109/TKDE.2022.3177212

    Article  Google Scholar 

  64. Wang H, Ren H, Leskovec J (2021) Relational message passing for knowledge graph completion. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining, pp 1697–1707

  65. Xu X, Zhang P, He Y, et al (2022) Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs. arXiv preprint https://doi.org/10.48550/arXiv.2208.00850

  66. Chen Z, Yu H, Li J, et al (2022) Entity representation by neighboring relations topology for inductive relation prediction. In: Proceedings of the 19th pacific rim international conference on artificial intelligence, pp 59–72

  67. Lin Q, Liu J, Xu F, et al (2022) Incorporating context graph with logical reasoning for inductive relation prediction. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 893–903

  68. Geng Y, Chen J, Zhang W, et al (2022) Relational message passing for fully inductive knowledge graph completion. arXiv preprint https://doi.org/10.48550/arXiv.2210.03994

  69. Wu J, Mai S, Hu H (2022) Relation-dependent contrastive learning with cluster sampling for inductive relation prediction. arXiv preprint https://doi.org/10.48550/arXiv.2211.12266

  70. Huang Q, Ren H, Leskovec J (2022) Few-shot relational reasoning via connection subgraph pretraining. arXiv preprint https://doi.org/10.48550/arXiv.2210.06722

  71. Bhowmik R, de Melo G (2020) Explainable link prediction for emerging entities in knowledge graphs. In: Proceedings of the 19th international semantic web conference, pp 39–55

  72. He Y, Wang Z, Zhang P, et al (2020) Vn network: embedding newly emerging entities with virtual neighbors. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 505-514

  73. Cui Y, Wang Y, Sun Z, et al (2022) Inductive knowledge graph reasoning for multi-batch emerging entities. In: Proceedings of the 31st ACM international conference on information and knowledge management, pp 335–344

  74. Xiong W, Yu M, Chang S, et al (2018) One-shot relational learning for knowledge graphs. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 1980–1990

  75. Jiang Z, Gao J, Lv X (2021) Metap: Meta pattern learning for one-shot knowledge graph completion. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 2232–2236

  76. Xu J, Zhang J, Ke X, et al (2021) P-int: a path-based interaction model for few-shot knowledge graph completion. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 385–394

  77. Wu Y, Tian L, Hui B, et al (2022) Learning discriminative representation for few-shot knowledge graph completion. In: Proceedings of the 7th international conference on intelligent information processing, pp 1–5

  78. Chen M, Zhang W, Zhang W, et al (2019) Meta relational learning for few-shot link prediction in knowledge graphs. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 4217–4226

  79. Niu G, Li Y, Tang C, et al (2021) Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 213–222

  80. Wu H, Yin J, Rajaratnam B, et al (2022) Hierarchical relational learning for few-shot knowledge graph completion. arXiv preprint https://doi.org/10.48550/arXiv.2209.01205

  81. Lv X, Gu Y, Han X, et al (2019) Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3376–3381

  82. Zhang C, Yu L, Saebi M, et al (2020) Few-shot multi-hop relation reasoning over knowledge bases. In: Proceedings of the 2020 conference on empirical methods in natural language processing, pp 580–585

  83. Qin P, Wang X, Chen W, et al (2020) Generative adversarial zero-shot relational learning for knowledge graphs. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 8673–8680

  84. Geng Y, Chen J, Chen Z, et al (2021) Ontozsl: ontology-enhanced zero-shot learning. In: Proceedings of the 30th world wide web conference, pp 3325–3336

  85. Liu X, Guo Y, Huang M, et al (2022) Stochastic and dual adversarial gan-boosted zero-shot knowledge graph. In: Proceedings of the 2nd CAAI international conference on artificial intelligence, pp 55–67

  86. Li X, Ma J, Yu J et al (2023) A structure-enhanced generative adversarial network for knowledge graph zero-shot relational learning. Inf Sci 629:169–183

    Article  Google Scholar 

  87. Li X, Ma J, Yu J et al (2022) Hapzsl: a hybrid attention prototype network for knowledge graph zero-shot relational learning. Neurocomputing 508:324–336

    Article  Google Scholar 

  88. Song R, He S, Zheng S, et al (2022a) Ontology-guided and text-enhanced representation for knowledge graph zero-shot relational learning. In: Proceedings of the 10th international conference on learning representations (ICLR) on deep learning on graphs for natural language processing

  89. Song R, He S, Zheng S, et al (2022b) Decoupling mixture-of-graphs: Unseen relational learning for knowledge graph completion by fusing ontology and textual experts. In: Proceedings of the 29th international conference on computational linguistics, pp 2237–2246

  90. Xie W, Wang S, Wei Y, et al (2020) Dynamic knowledge graph completion with jointly structural and textual dependency. In: Proceedings of the 20th international conference on algorithms and architectures for parallel processing, pp 432–448

  91. Chen X, Jia S, Ding L et al (2020) Sdt: an integrated model for open-world knowledge graph reasoning. Expert Syst Appl 162:113889

    Article  Google Scholar 

  92. Shah H, Villmow J, Ulges A, et al (2019) An open-world extension to knowledge graph completion models. In: Proceedings of the 33th AAAI conference on artificial intelligence, pp 3044–3051

  93. Zhou Y, Shi S, Huang H (2020) Weighted aggregator for the open-world knowledge graph completion. In: Proceedings of the 6th international conference of pioneering computer scientists, engineers and educators, pp 283–291

  94. Shah H, Villmow J, Ulges A (2020) Relation specific transformations for open world knowledge graph completion. In: Proceedings of the graph-based methods for natural language processing (TextGraphs), pp 78–84

  95. Zhu W, Zhi X, Tong W (2020) Extracting short entity descriptions for open-world extension to knowledge graph completion models. In: Proceedings of the 13th international conference on knowledge science, engineering and management, pp 16–27

  96. Wang Y, Xiao W, Tan Z et al (2021) Caps-owkg: a capsule network model for open-world knowledge graph. Int J Mach Learn Cybern 12:1627–1637

    Article  Google Scholar 

  97. Wang J, Lei J, Sun S et al (2022) Embeddings based on relation-specific constraints for open world knowledge graph completion. Appl Intell 53(12):16192–16204

    Article  Google Scholar 

  98. Wang Z, Lai K, Li P, et al (2019) Tackling long-tailed relations and uncommon entities in knowledge graph completion. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 250–260

  99. Oh B, Seo S, Hwang J et al (2022) Open-world knowledge graph completion for unseen entities and relations via attentive feature aggregation. Inf Sci 586:468–484

    Article  Google Scholar 

  100. Clouatre L, Trempe P, Zouaq A, et al (2021) Mlmlm: link prediction with mean likelihood masked language model. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 4321–4331

  101. Wang X, Gao T, Zhu Z et al (2021) Kepler: a unified model for knowledge embedding and pre-trained language representation. Trans Assoc Comput Linguist 9:176–194

    Article  CAS  Google Scholar 

  102. Daza D, Cochez M, Groth P (2021) Inductive entity representations from text via link prediction. In: Proceedings of the 30th world wide web conference, pp 798–808

  103. Markowitz E, Balasubramanian K, Mirtaheri M, et al (2022) Statik: Structure and text for inductive knowledge graph completion. In: Proceedings of the 2022 conference of the North American chapter of the association for computational linguistics, pp 604–615

  104. Zhang N, Xie X, Chen X, et al (2022) Reasoning through memorization: Nearest neighbor knowledge graph embeddings. arXiv preprint https://doi.org/10.48550/arXiv.2201.05575

  105. Wang L, Zhao W, Wei Z, et al (2022) Simkgc: Simple contrastive knowledge graph completion with pre-trained language models. In: Proceedings of the 60th annual meeting of the association for computational linguistics, pp 4281–4294

  106. Lv X, Lin Y, Cao Y, et al (2022) Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach. In: Proceedings of the 60th annual meeting of the association for computational linguistics, pp 3570–3581

  107. Peng B, Liang S, Islam M (2022) Bi-link: Bridging inductive link predictions from text via contrastive learning of transformers and prompts. arXiv preprint https://doi.org/10.48550/arXiv.2210.14463

  108. Li D, Yang S, Xu K, et al (2022) Multi-task pre-training language model for semantic network completion. arXiv preprint https://doi.org/10.48550/arXiv.2201.04843

  109. Nadkarni R, Wadden D, Beltagy I, et al (2021) Scientific language models for biomedical knowledge base completion: an empirical study. arXiv preprint https://doi.org/10.48550/arXiv.2106.09700

  110. Wu J, Mai S, Hu H (2022) Contextual relation embedding and interpretable triplet capsule for inductive relation prediction. Neurocomputing 505:80–91

    Article  Google Scholar 

  111. Lin Q, Mao R, Liu J et al (2023) Fusing topology contexts and logical rules in language models for knowledge graph completion. Inf Fusion 90:253–264

    Article  Google Scholar 

  112. Wang B, Shen T, Long G, et al (2021) Structure-augmented text representation learning for efficient knowledge graph completion. In: Proceedings of the 30th world wide web conference, pp 1737–1748

  113. Zha H, Chen Z, Yan X (2022) Inductive relation prediction by bert. In: Proceedings of the 36th AAAI conference on artificial intelligence, pp 5923–5931

  114. V K, Tripathi B, Khapra MM, et al (2021) A joint training framework for open-world knowledge graph embeddings. In: Proceedings of the 3rd conference on automated knowledge base construction

  115. Wang B, Wang G, Huang J, et al (2021) Inductive learning on commonsense knowledge graph completion. In: Proceedings of the 2021 international joint conference on neural networks (IJCNN), pp 1–8

  116. Xie R, Liu Z, Luan H, et al (2017) Image-embodied knowledge representation learning. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3140–3146

  117. Pezeshkpour P, Chen L, Singh S (2018) Embedding multimodal relational data for knowledge base completion. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3208–3218

  118. Wang Z, Li L, Li Q, et al (2019) Multimodal data enhanced representation learning for knowledge graphs. In: Proceedings of the 2019 international joint conference on neural networks (IJCNN), pp 1–8

  119. Liang S, Zhu A, Zhang J et al (2023) Hyper-node relational graph attention network for multi-modal knowledge graph completion. ACM Trans Multimed Comput Commun Appl 19(2):1–21

    Article  Google Scholar 

  120. Zheng S, Wang W, Qu J, et al (2022) Mmkgr: multi-hop multi-modal knowledge graph reasoning. arXiv preprint https://doi.org/10.48550/arXiv.2209.01416

  121. Hao Y, Cao X, Fang Y, et al (2021) Inductive link prediction for nodes having only attribute information. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 1209–1215

  122. Li Y, He D, Ban Z (2022) Learning node embedding for inductive link prediction in sparse observation network. In: Proceedings of the 2022 international joint conference on neural networks (IJCNN), pp 1–7

  123. Zhang D, Yin J, Yu PS (2022) Link prediction with contextualized self-supervision. In: IEEE transactions on knowledge and data engineering, pp 1–14. https://doi.org/10.1109/TKDE.2022.3200390

  124. Hu Z, Gutiérrez-Basulto V, Xiang Z, et al (2022) Type-aware embeddings for multi-hop reasoning over knowledge graphs. arXiv preprint https://doi.org/10.48550/arXiv.2205.00782

  125. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30

  126. Zeng H, Zhou H, Srivastava A, et al (2019) Graphsaint: Graph sampling based inductive learning method. arXiv preprint https://doi.org/10.48550/arXiv.1907.04931

  127. Mikolov T, Chen K, Corrado G, et al (2013) Efficient estimation of word representations in vector space. arXiv preprint https://doi.org/10.48550/arXiv.1301.3781

  128. Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  129. Devlin J, Chang MW, Lee K, et al (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint https://doi.org/10.48550/arXiv.1810.04805

  130. Liu Y, Ott M, Goyal N, et al (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint https://doi.org/10.48550/arXiv.1907.11692

  131. Auer S, Bizer C, Kobilarov G, et al (2007) Dbpedia: a nucleus for a web of open data. In: Proceedings of the 6th international semantic web conference and the 2nd Asian semantic web conference, pp 722–735

  132. Bollacker K, Evans C, Paritosh P, et al (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, pp 1247–1250

Download references

Acknowledgements

This research is supported by National Natural Science Foundation of China (Grant No.: 61375084), and the Shandong Natural Science Foundation of China (Grant No.: ZR2019MF064). Thanks are due to all the anonymous reviewers.

Author information

Authors and Affiliations

  1. School of Information Science and Electrical Engineering, Shangdong Jiaotong University, Jinan, 250357, Shandong, China

    Xinyu Liang, Guannan Si, Jianxin Li, Pengxin Tian & Zhaoliang An

  2. School of Control Science and Engineering, Shandong University, Jinan, 250000, Shandong, China

    Fengyu Zhou

Authors
  1. Xinyu Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guannan Si
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengxin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhaoliang An
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fengyu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guannan Si.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, X., Si, G., Li, J. et al. A survey of inductive knowledge graph completion. Neural Comput & Applic 36, 3837–3858 (2024). https://doi.org/10.1007/s00521-023-09286-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09286-2

Keywords

Search

Navigation