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Machine Learning for Refining Knowledge Graphs: A Survey

Subagdja, Budhitama, Shanthoshigaa, D., Wang, Zhaoxia, Tan, Ah-Hwee·2024·ACM Computing Surveys
DOI 10.1145/3640313
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Abstract

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in KGs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this article presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into the development of fully automated KG refinement.

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  1. 10.1016/j.jnca.2021.103076 · DOI
  2. 10.1109/ACCESS.2020.2973928 · DOI
  3. Samur Araújo, Duc Tran, Arjen DeVries, Jan Hidders, and Daniel Schwabe. 2012. SERIMI: Class-based disambiguation for effective instance matching over heterogeneous web data. In Proceedings of the 15th International Workshop on the Web and Databases 2012, Zachary G. Ives and Yannis Velegrakis (Eds.). 25–30. Retrieved from http://db.disi.unitn.eu/pages/WebDB2012/papers/p4.pdf
  4. Siddhant Arora. 2020. A survey on graph neural networks for knowledge graph completion. arxiv:2007.12374. Retrieved from http://arxiv.org/abs/2007.12374 · arXiv
  5. Caleb Belth Xinyi Zheng Jilles Vreeken and Danai Koutra. 2020. What is normal what is strange and what is missing in a knowledge graph: Unified characterization via inductive summarization. Proceedings of the Web Conference (WWW’20) 1115–1126. DOI:10.1145/3366423.3380189 · DOI
  6. 10.1109/tpami.2013.50 · DOI · Paper page
  7. 10.5555/944919.944966 · DOI
  8. 10.1007/s00778-008-0098-x · DOI
  9. 10.1145/956750.956759 · DOI
  10. 10.1109/MIS.2009.102 · DOI
  11. 10.1007/s10994-013-5363-6 · DOI · Paper page
  12. 10.5555/2999792.2999923 · DOI
  13. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations 2014, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1312.6203 · arXiv
  14. 10.1145/2567948.2578999 · DOI
  15. 10.1609/aaai.v24i1.7519 · DOI · Paper page
  16. 10.1145/3357384.3357879 · DOI
  17. 10.1109/access.2020.3030076 · DOI
  18. Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akhsay Krishnamurthy, and Alexander J. Smola. 2018. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning.. In Proceedings of the 6th International Conference on Learning Representations. 18.
  19. 10.18653/v1/2020.findings-emnlp.427 · DOI
  20. 10.18653/v1/D18-1225 · DOI · Paper page
  21. 10.3233/SW-2011-0034 · DOI
  22. 10.1609/aaai.v32i1.11573 · DOI · Paper page
  23. 10.1145/2623330.2623623 · DOI · Paper page
  24. 10.1145/3097983.3098036 · DOI
  25. 10.1007/978-3-319-07443-6_20 · DOI
  26. 10.14778/3236187.3236198 · DOI
  27. 10.4018/jswis.2011070103 · DOI
  28. 10.1145/2488388.2488425 · DOI
  29. 10.1007/978-3-642-35176-1_5 · DOI
  30. 10.14778/2367502.2367564 · DOI
  31. 10.1609/aaai.v34i04.5815 · DOI · Paper page
  32. 10.1145/2939672.2939754 · DOI · Paper page
  33. Lingbing Guo, Qingheng Zhang, Weiyi Ge, Wei Hu, and Yuzhong Qu. 2019. DSKG: A deep sequential model for knowledge graph completion. In Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding, Jun Zhao, Frank van Harmelen, Jie Tang, Xianpei Han, Quan Wang, and Xianyong Li (Eds.). Springer, 65–77.
  34. 10.1007/978-3-031-01588-5_2 · DOI
  35. 10.5555/3294771.3294869 · DOI
  36. Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Semi-implicit graph variational auto-encoders. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Article 961, 10712–10723 pages. Retrieved from https://proceedings.neurips.cc/paper/2019/file/fd4771e85e1f916f239624486bff502d-Paper.pdf
  37. 10.18653/v1/2021.findings-acl.412 · DOI
  38. 10.1145/3366423.3380027 · DOI · Paper page
  39. 10.3389/fbioe.2020.00074 · DOI
  40. 10.3115/v1/p15-1067 · DOI
  41. 10.1609/aaai.v30i1.10089 · DOI
  42. 10.1145/3308558.3313586 · DOI
  43. 10.1007/978-3-030-12375-8 · DOI
  44. 10.1109/ICDM.2013.60 · DOI
  45. 10.1007/978-3-319-18818-8_24 · DOI
  46. 10.1016/j.websem.2015.07.002 · DOI
  47. Mayank A. Kejriwal, Knoblock Craig, and Pedro Szekely. 2021. Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press, Cambridge.
  48. 10.18653/v1/2020.coling-main.153 · DOI
  49. Thomas Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv: 1611.07308. Retrieved from https://arxiv.org/abs/1611.07308 · arXiv
  50. 10.3233/SW-180316 · DOI
  51. Christian Kolthoff and Arnab Dutta. 2015. Semantic relation composition in large scale knowledge bases. In Proceedings of the 3rd International Workshop on Linked Data for Information Extraction. Gentile, Anna Lisa, Aachen, 34–47.
  52. 10.1007/978-3-319-75420-8_1 · DOI
  53. Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020. Tensor decompositions for temporal knowledge base completion. In Proceedings of the 8th International Conference on Learning Representations. OpenReview.net. Retrieved from https://openreview.net/forum?id=rke2P1BFwS
  54. 10.1145/1871437.1871698 · DOI
  55. 10.1145/3184558.3191639 · DOI · Paper page
  56. 10.1007/s00521-019-04181-1 · DOI
  57. 10.1007/978-3-319-22849-5_5 · DOI
  58. 10.1109/ICDMW.2018.00135 · DOI
  59. 10.1364/OFC.2020.Th1F.5 · DOI
  60. 10.1016/j.ins.2022.06.023 · DOI
  61. Yankai Lin Xu Han Ruobing Xie Zhiyuan Liu and Maosong Sun. 2018. Knowledge representation learning: A quantitative review. arxiv:1812.10901. Retrieved from http://arxiv.org/abs/1812.10901 · arXiv
  62. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. Proceedings of the National Conference on Artificial Intelligence 3, 1 (2015), 2181–2187.
  63. 10.5555/3305890.3305905 · DOI
  64. 10.1007/978-3-319-71249-9_43 · DOI
  65. 10.1007/978-3-662-45495-4_3 · DOI
  66. 10.1016/j.eswa.2019.113120 · DOI
  67. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems -. Curran Associates Inc., Red Hook, NY, USA, 3111–3119.
  68. 10.3115/1690219.1690287 · DOI
  69. 10.5555/541177 · DOI
  70. 10.1145/3183713.3196926 · DOI
  71. 10.1145/1935826.1935869 · DOI
  72. Annamalai Narayanan Mahinthan Chandramohan Rajasekar Venkatesan Lihui Chen Yang Liu and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. arXiv: 1707.05005. Retrieved from https://arxiv.org/abs/1707.05005 · arXiv
  73. 10.3233/SW-150210 · DOI
  74. 10.18653/v1/2020.textgraphs-1.1 · DOI
  75. 10.5555/3104482.3104584 · DOI
  76. 10.1145/2187836.2187874 · DOI · Paper page
  77. 10.1007/978-3-642-30284-8_15 · DOI
  78. Andrea Giovanni Nuzzolese, Aldo Gangemi, Valentina Presutti, and Paolo Ciancarini. 2012. Type inference through the analysis of Wikipedia links. In Proceedings of the World Wide Web 2012 Workshop on Linked Data on the Web. 1–9. Retrieved from http://ceur-ws.org/Vol-937/
  79. 10.1109/TKDE.2019.2941685 · DOI
  80. 10.1109/69.917563 · DOI
  81. 10.1007/978-3-642-38288-8_27 · DOI
  82. Heiko Paulheim. 2012. Browsing linked open data with auto complete. In Proceedings of the 10th Semantic Web Challenge 2012 at the 11th International Semantic Web Conference. 1–8.
  83. 10.3233/SW-160218 · DOI · Paper page
  84. 10.1007/978-3-642-41335-3_32 · DOI
  85. 10.4018/ijswis.2014040104 · DOI
  86. 10.1145/2623330.2623732 · DOI · Paper page
  87. 10.1007/978-3-642-41335-3_34 · DOI
  88. 10.1145/3159652.3159706 · DOI
  89. 10.1007/978-3-030-49461-2_16 · DOI · Paper page
  90. 10.1007/978-3-319-93417-4_38 · DOI · Paper page
  91. 10.1609/aaai.v33i01.33013060 · DOI · Paper page
  92. Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao. 2018. M-Walk: Learning to walk over graphs using Monte Carlo tree search. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, 6787–6798.
  93. 10.18653/v1/2020.emnlp-main.131 · DOI
  94. Baoxu Shi and Tim Weninger. 2018. Open-world knowledge graph completion. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence , 1957–1964.
  95. 10.1109/ICSC.2013.22 · DOI
  96. Jennifer Sleeman, Tim Finin, and Anupam Joshi. 2015. Topic modeling for RDF graphs. In Proceedings of the 3rd International Workshop on Linked Data for Information Extraction. CEUR Workshop Proceedings, 48–62.
  97. 10.5555/2999611.2999715 · DOI
  98. 10.5555/2999611.2999715 · DOI
  99. 10.1145/2660517.2660532 · DOI
  100. Zhiqing Sun, Zhi Hong Deng, Jian Yun Nie, and Jian Tang. 2019. RotatE: Knowledge graph embedding by relational rotation in complex space. In Proceedings of the 7th International Conference on Learning Representations.
  101. Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA.
  102. 10.1145/2736277.2741093 · DOI · Paper page
  103. 10.1145/3383972.3384067 · DOI
  104. Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. In Proceedings of the 34th International Conference on Machine Learning - Volume 70. JMLR.org, 3462–3471.
  105. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In Proceedings of The 33rd International Conference on Machine Learning (Proceedings of Machine Learning Research), Maria Florina Balcan and Kilian Q. Weinberger (Eds.), Vol. 48. PMLR, 2071–2080. Retrieved from https://proceedings.mlr.press/v48/trouillon16.html
  106. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR ’18). Retrieved from https://openreview.net/forum?id=rJXMpikCZ
  107. 10.24963/ijcai.2020/267 · DOI
  108. 10.1145/3442381.3450043 · DOI
  109. 10.1109/IJCNN52387.2021.9534355 · DOI
  110. 10.1145/3391298 · DOI
  111. 10.1145/2939672.2939753 · DOI · Paper page
  112. 10.1016/j.knosys.2020.106421 · DOI
  113. 10.1109/TKDE.2017.2754499 · DOI · Paper page
  114. 10.1007/BF00992698 · DOI · Paper page
  115. 10.1109/SKG.2016.020 · DOI
  116. 10.1007/978-3-319-07443-6_34 · DOI
  117. 10.1080/10919392.2018.1517481 · DOI
  118. 10.1145/3404835.3462961 · DOI
  119. 10.1109/tai.2021.3076021 · DOI
  120. 10.18653/v1/p16-1219 · DOI
  121. Xiao Lin, Pero Subasic, and Hongfeng Yin. 2020. Rel4KC: A reinforcement learning agent for knowledge graph completion and validation. In Proceedings of the Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1291. Retrieved from http://www.cse.msu.edu/zhaoxi35/DRL4KDD/1.pdf
  122. 10.18653/v1/2021.naacl-main.333 · DOI
  123. 10.18653/v1/d17-1060 · DOI
  124. 10.1109/TKDE.2022.3150080 · DOI · Paper page
  125. Yexiang Xue, Yang Yuan, Zhitian Xu, and Ashish Sabharwal. 2018. Expanding holographic embeddings for knowledge completion. In Proceedings of the Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper/2018/file/dd28e50635038e9cf3a648c2dd17ad0a-Paper.pdf
  126. 10.1109/ACCESS.2020.2977136 · DOI
  127. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). Retrieved from http://arxiv.org/abs/1412.6575 · arXiv
  128. 10.1360/n112017-00145 · DOI
  129. Liang Yao Chengsheng Mao and Yuan Luo. 2019. KG-BERT: BERT for knowledge graph completion. arXiv: 1909.03193. Retrieved from http://arxiv.org/abs/1909.03193 · arXiv
  130. 10.1109/tbdata.2018.2850013 · DOI · Paper page
  131. 10.24963/ijcai.2019/594 · DOI
  132. 10.1016/j.jbi.2021.103696 · DOI
  133. Yunan Zhang Xiang Cheng Heting Gao and ChengXiang Zhai. 2019. Cooperative reasoning on knowledge graph and corpus: A multi-agent reinforcement learning approach. arXiv:1912.02206. Retrieved from https://arxiv.org/abs/1912.02206 · arXiv
  134. 10.1609/aaai.v34i03.5701 · DOI
  135. 10.1007/s10618-015-0430-1 · DOI
  136. 10.1145/3132847.3132939 · DOI
  137. 10.2200/S00196ED1V01Y200906AIM006 · DOI
  138. 10.18653/v1/d19-1268 · DOI
  139. 10.1088/1742-6596/1487/1/012016 · DOI

Metadata

Authors
Subagdja, B., Shanthoshigaa, D., Wang, Z., & Tan, A.-H.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2024-02-23
Volume
56
Issue
6
Pages
1-38
ISSN
  • 0360-0300
  • 1557-7341
DOI
10.1145/3640313
Cited by
24
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • A*STAR under its Advanced Manufacturing and Engineering (AME) Programmatic Grant (A19E2b0098)
  • Jubilee Technology Fellowship awarded to Ah-Hwee Tan by Singapore Management University

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CoChat. (2026). Citation check: Machine Learning for Refining Knowledge Graphs: A Survey. https://app.cochat.ai/papers/10.1145/3640313
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Subagdja, B., Shanthoshigaa, D., Wang, Z., & Tan, A.-H. (2024). Machine Learning for Refining Knowledge Graphs: A Survey. ACM Computing Surveys. https://doi.org/10.1145/3640313