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Uncertain knowledge graph embedding: an effective method combining multi-relation and multi-path

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Abstract

Uncertain Knowledge Graphs (UKGs) are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs. The research on the embedding of UKG has only recently begun, Uncertain Knowledge Graph Embedding (UKGE) model has a certain effect on solving this problem. However, there are still unresolved issues. On the one hand, when reasoning the confidence of unseen relation facts, the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information, leading to information loss. On the other hand, the existing UKG embedding model can only model symmetric relation facts, but the embedding problem of asymmetric relation facts has not be addressed. To address the above issues, a Multiplex Uncertain Knowledge Graph Embedding (MUKGE) model is proposed in this paper. First, to combine multiple information and achieve more accurate results in confidence reasoning, the Uncertain ResourceRank (URR) reasoning algorithm is introduced. Second, the asymmetry in the UKG is defined. To embed asymmetric relation facts of UKG, a multi-relation embedding model is proposed. Finally, experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE. The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines, and it helps advance the research on UKG embedding.

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References

  1. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of 2008 ACM SIGMOD International Conference on Management of Data. 2008, 1247–1250

  2. Mitchell T, Cohen W, Hruschka E, Talukdar P, Yang B S, Betteridge J, Carlson A, Dalvi B, Gardner M, Kisiel B, Krishnamurthy J, Lao N, Mazaitis K, Mohamed T, Nakashole N, Platanios E, Ritter A, Samadi M, Settles B, Wang R, Wijaya D, Gupta A, Chen X, Saparov A, Greaves M, Welling J. Never-ending learning. Communications of the ACM, 2018, 61(5): 103–115

    Article  Google Scholar 

  3. Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes P N, Hellmann S, Morsey M, Van Kleef P, Auer S, Bizer C. Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web, 2015, 6(2): 167–195

    Article  Google Scholar 

  4. Wang D. Answering contextual questions based on ontologies and question templates. Frontiers of Computer Science in China, 2011, 5(4): 405–418

    Article  MathSciNet  Google Scholar 

  5. Zhong Z, Lin X, He L. Answering range-based reverse kNN and why-not reverse kNN queries. Frontiers of Computer Science, 2020, 14(1): 233–235

    Article  Google Scholar 

  6. Zhong Z, Lin X, He L, Yang J. Answering why-not questions on knn queries. Frontiers of Computer Science, 2019, 13(5): 1062–1071

    Article  Google Scholar 

  7. Wu J, He X, Wang X, Wang Q, Chen W, Lian J, Xie X. Graph convolution machine for context-aware recommender system. Frontiers of Computer Science, 2022, 16(6): 166614

    Article  Google Scholar 

  8. Zhang Z, Li C, Wu Z, Sun A, Ye D, Luo X. NEXT: a neural network framework for next poi recommendation. Frontiers of Computer Science, 2020, 14(2): 314–333

    Article  Google Scholar 

  9. Zheng Z, Liu Y, Li D, Zhang X. Distant supervised relation extraction based on residual attention. Frontiers of Computer Science, 2022, 16(6): 166336

    Article  Google Scholar 

  10. Cao Y, Chen D, Xu Z, Li H, Luo P. Nested relation extraction with iterative neural network. Frontiers of Computer Science, 2021, 15(3): 153323

    Article  Google Scholar 

  11. Xie T, Wu B, Jia B, Wang B. Graph-ranking collective Chinese entity linking algorithm. Frontiers of Computer Science, 2020, 14(2): 291–303

    Article  Google Scholar 

  12. Hu Y, Shen D, Nie T, Kou Y, Yu G. Biomedical entity linking based on less labeled data. Frontiers of Computer Science, 2022, 16(3): 163343

    Article  Google Scholar 

  13. Li M, Xing Y, Kong F, Zhou G. Towards better entity linking. Frontiers of Computer Science, 2022, 16(2): 162308

    Article  Google Scholar 

  14. Zeng K, Li C, Hou L, Li J, Feng L. A comprehensive survey of entity alignment for knowledge graphs. AI Open, 2021, 2: 1–13

    Article  Google Scholar 

  15. Lin Y, Han X, Xie R, Liu Z, Sun M. Knowledge representation learning: a quantitative review. 2018, arXiv preprint arXiv: 1812.10901v1

  16. Rebele T, Suchanek F, Hoffart J, Biega J, Kuzey E, Weikum G. Yago: a multilingual knowledge base from wikipedia, wordnet, and geonames. In: Proceedings of the 15th International Semantic Web Conference. 2016, 177–185

  17. Chang D, Chen M, Liu C, Liu L, Li D, Li W, Kong F, Liu B, Luo X, Qi J, Jin Q, Xu B. DiaKG: An annotated diabetes dataset for medical knowledge graph construction. In: Proceedings of the 6th China Conference on Knowledge Graph and Semantic Computing. 2021, 308–314

  18. Speer R, Chin J, Havasi C. ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017, 4444–4451

  19. Wu W, Li H, Wang H, Zhu K Q. Probase: a probabilistic taxonomy for text understanding. In: Proceedings of 2012 ACM SIGMOD International Conference on Management of Data. 2012, 481–492

  20. Chiachío M, Chiachío J, Prescott D, Andrews J. A new paradigm for uncertain knowledge representation by plausible petri nets. Information Sciences, 2018, 453: 323–345

    Article  MathSciNet  Google Scholar 

  21. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 2019, 9(4): e1312

    Article  Google Scholar 

  22. Raskolnikov A. Probabilistic compliance. Yale Journal on Regulation, 2017, 34(2): 492–493

    Google Scholar 

  23. Yang S, Zhang W, Tang R, Zhang M, Huang Z. Approximate inferring with confidence predicting based on uncertain knowledge graph embedding. Information Sciences, 2022, 609: 679–690

    Article  Google Scholar 

  24. Chen X, Boratko M, Chen M, Dasgupta S S, Li X L, McCallum A. Probabilistic box embeddings for uncertain knowledge graph reasoning. In: Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021

  25. Dubois D, Prade H. Upper and lower possibilities induced by a multivalued mapping. IFAC Proceedings Volumes, 1983, 16(13): 147–152

    Article  Google Scholar 

  26. Shafer G A. A Mathematical Theory of Evidence. Princeton, NJ: Princeton University Press, 1976

    Book  Google Scholar 

  27. Pawlak Z. Rough sets. International Journal of Computer & Information Sciences, 1982, 11(5): 341–356

    Article  MathSciNet  Google Scholar 

  28. Xu T H, Wang G Y. Finding strongly connected components of simple digraphs based on generalized rough sets theory. Knowledge-Based Systems, 2018, 149: 88–98

    Article  Google Scholar 

  29. Zhang Z, Yang X. Tolerance-based multigranulation rough sets in incomplete systems. Frontiers of Computer Science, 2014, 8(5): 753–762

    Article  MathSciNet  Google Scholar 

  30. Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine. Mathematical Biosciences, 1975, 23(3–4): 351–379

    Article  MathSciNet  Google Scholar 

  31. Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338–353

    Article  MathSciNet  Google Scholar 

  32. Yang J, Wang G Y, Zhang Q H, Chen Y H, Xu T H. Optimal granularity selection based on cost-sensitive sequential three-way decisions with rough fuzzy sets. Knowledge-Based Systems, 2019, 163: 131–144

    Article  Google Scholar 

  33. Zhang Q H, Xia D Y, Liu K X, Wang G Y. A general model of decision-theoretic three-way approximations of fuzzy sets based on a heuristic algorithm. Information Sciences, 2020, 507: 522–539

    Article  Google Scholar 

  34. Zhang Q H, Chen Y H, Yang J, Wang G Y. Fuzzy entropy: A more comprehensible perspective for interval shadowed sets of fuzzy sets. IEEE Transactions on Fuzzy Systems, 2020, 28(11): 3008–3022

    Article  Google Scholar 

  35. Duda R, Gaschnig J, Hart P. Model design in the PROSPECTOR consultant system for mineral exploration. In: Webber B L, Nilsson N J, eds. Readings in Artificial Intelligence. San Mateo, CA: Elsevier, 1981

    Google Scholar 

  36. Chen F, Wang Y, Wang B, Kuo C C J. Graph representation learning: a survey. 2019, arXiv preprint arXiv: 1909.00958

  37. Ji S, Pan S, Cambria E, Marttinen P, Yu P S. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494–514

    Article  MathSciNet  Google Scholar 

  38. Wang Q, Mao Z D, Wang B, Guo L. Knowledge graph embedding: a survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724–2743

    Article  Google Scholar 

  39. Yan J H, Wang C Y, Cheng W L, Gao M, Zhou A Y. A retrospective of knowledge graphs. Frontiers of Computer Science, 2018, 12(1): 55–74

    Article  Google Scholar 

  40. Doh R F, Zhou C H, Arthur J K, Tawiah I, Doh B. A systematic review of deep knowledge graph-based recommender systems, with focus on explainable embeddings. Data, 2022, 7(7): 94

    Article  Google Scholar 

  41. Zhang H, Zheng T, Gao J, Miao C, Su L, Li Y, Ren K. Data poisoning attack against knowledge graph embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019

  42. Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2787–2795

  43. Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1112–1119

  44. Ji G, He S, Xu L, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015, 687–696

  45. Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2181–2187

  46. Nickel M, Tresp V, Kriegel H P. A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on International Conference on Machine Learning. 2011, 809–816

  47. Yang B, Yih W T, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations. 2015, 1–12

  48. Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016, 1955–1961

  49. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G. Complex embeddings for simple link prediction. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. 2016, 2071–2080

  50. Sun Z, Deng Z, Nie J, Tang J. Rotate: knowledge graph embedding by relational rotation in complex space. In: Proceedings of International Conference on Learning Representations. 2019

  51. Song T, Luo J, Huang L. Rot-pro: modeling transitivity by projection in knowledge graph embedding. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 24695–24706

  52. Chao L, He J, Wang T, Chu W. PairRE: knowledge graph embeddings via paired relation vectors. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 2021

  53. Li Z, Liu H, Zhang Z, Liu T, Xiong N. Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(8): 3961–3973

    Article  MathSciNet  Google Scholar 

  54. Zhang Z, Li Z, Liu H, Xiong N. Multi-scale dynamic convolutional network for knowledge graph embedding. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 2335–2347

    Article  Google Scholar 

  55. Chen Z, Yeh M Y, Kuo T. PASSLEAF: a pool-based semi-supervised learning framework for uncertain knowledge graph embedding. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4019–4026

  56. Chen X, Boratko M, Chen M, Dasgupta S S, Li X L, McCallum A. Probabilistic box embeddings for uncertain knowledge graph reasoning. In: Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021

  57. Yang S, Zhang W, Tang R. Fast confidence prediction of uncertainty based on knowledge graph embedding. In: Proceedings of the 3rd International Conference on Algorithms, Computing and Artificial Intelligence. 2020, 54

  58. Liu F, Shen Y, Zhang T, Gao H. Entity-related paths modeling for knowledge base completion. Frontiers of Computer Science, 2020, 14(5): 145311

    Article  Google Scholar 

  59. Hommersom A, Lucas P J F. An introduction to knowledge representation and reasoning in healthcare. In: Hommersom A, Lucas P J F, eds. Foundations of Biomedical Knowledge Representation. Cham: Springer, 2015, 9–32

    Chapter  Google Scholar 

  60. Zhang J, Wu T, Qi G. Gaussian metric learning for few-shot uncertain knowledge graph completion. In: Proceedings of the 26th International Conference on Database Systems for Advanced Applications. 2021, 256–271

  61. Chen X, Chen M, Shi W, Sun Y, Zaniolo C. Embedding uncertain knowledge graphs. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence and the 31st Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. 2019, 413

  62. Galárraga L A, Teflioudi C, Hose K, Suchanek F. Amie: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web. 2013, 413–422

  63. Kimmig A, Bach S H, Broecheler M, Huang B, Getoor L. A short introduction to probabilistic soft logic. In: Proceedings of the 26th Neural Information Processing Systems. 2012, 1–4

  64. El Halaby M, Abdalla A. New phase transitions for formulas in Łukasiewicz logic. Frontiers of Computer Science, 2020, 14(6): 146403

    Article  Google Scholar 

  65. Zhang Z, Cai J, Zhang Y, Wang J. Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 3065–3072

  66. Liu H, Wu Y, Yang Y. Analogical inference for multi-relational embeddings. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 2168–2178

  67. Xu W, Zheng S, He L, Shao B, Yin J, Liu T. SEEK: segmented embedding of knowledge graphs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020

  68. Kazemi S M, Poole D. Simple embedding for link prediction in knowledge graphs. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018

  69. Fan M, Zhou Q, Zheng T F. Learning embedding representations for knowledge inference on imperfect and incomplete repositories. In: Proceedings of 2016 IEEE/WIC/ACM International Conference on Web Intelligence. 2016, 42–48

  70. Kertkeidkachorn N, Liu X, Ichise R. GTransE: generalizing translation-based model on uncertain knowledge graph embedding. In: Proceedings of the 33rd Annual Conference of the Japanese Society for Artificial Intelligence. 2019, 170–178

  71. Hu J, Cheng R, Huang Z, Fang Y, Luo S. On embedding uncertain graphs. In: Proceedings of the 26th ACM on Conference on Information and Knowledge Management. 2017, 157–166

  72. Zhu YanLi, Yang XiaoPing, Wang Liang, Zang ZhiYu. TransRD: embedding of knowledge graph with asymmetric features (in Chinese). Journal of Chinese Information Processing, 2019, 33(11): 73–82

    Google Scholar 

  73. Jia S, Xiang Y, Chen X, Wang K, Shi J. Triple trustworthiness measurement for knowledge graph. In: Proceedings of 2019 World Wide Web Conference. 2019, 2865–2871

  74. Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A, Wiener J. Graph structure in the web. Computer Networks, 2000, 33(1–6): 309–320

    Article  Google Scholar 

  75. Page L, Brin S, Motwani R, Winograd T. The pagerank citation ranking: bringing order to the Web. Stanford InfoLab. 1999

  76. Wu H, Pei Y, Yu J. Detecting academic experts by topic-sensitive link analysis. Frontiers of Computer Science in China, 2009, 3(4): 445–456

    Article  Google Scholar 

  77. Balazevic I, Allen C, Hospedales T M. Tucker: Tensor factorization for knowledge graph completion. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019

  78. Zou Y, Qiu D. Combining tensor decomposition and word embedding for asymmetrical relationship prediction in knowledge graphs. In: Proceedings of the 13th International Symposium on Computational Intelligence and Design. 2020

  79. Chen M, Weinberger K Q, Xu Z, Sha F. Marginalizing stacked linear denoising autoencoders. The Journal of Machine Learning Research, 2015, 16(1): 3849–3875

    MathSciNet  Google Scholar 

  80. Szklarczyk D, Morris J H, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva N T, Roth A, Bork P, Jensen L J, Von Mering C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Research, 2017, 45(D1): D362–D368

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (Nos. 2020YFC2003502, 2021YFF0704101), the National Natural Science Foundation of China (Grant No. 62276038), the Natural Science Foundation of Chongqing (Nos. cstc2019jcyj-cxttX0002, cstc2021ycjh-bgzxm0013) and the Key Cooperation Project of Chongqing Municipal Education Commission (HZ20210-08).

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Authors and Affiliations

  1. Key Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qi Liu, Qinghua Zhang, Fan Zhao & Guoyin Wang

  2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qi Liu, Qinghua Zhang, Fan Zhao & Guoyin Wang

  3. Chongqing Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qi Liu, Qinghua Zhang, Fan Zhao & Guoyin Wang

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  1. Qi Liu
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  2. Qinghua Zhang
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Correspondence to Qinghua Zhang.

Additional information

Qi Liu received the BE degree from Chongqing University of Posts and Telecommunications, China in 2020. She is currently pursuing the ME degree in Chongqing University of Posts and Telecommunications, China. Her research interests include knowledge graph construction, knowledge representation and uncertain reasoning.

Qinghua Zhang received the BS degree from the Sichuan University, China in 1998, MS degree from Chongqing University of Posts and Telecommunications, China in 2003, and the PhD degree from the Southwest Jiaotong University, China in 2010. He was at San Jose State University, USA, as a visiting scholar in 2015. Since 1998, he has been at the Chongqing University of Posts and Telecommunications, China where he is currently a professor, and the director of the Science and Technology Division. His research interests include rough set, fuzzy set, granular computing and uncertain information processing. He is a member of the IEEE.

Fan Zhao received the BS and MS degrees from Chongqing University of Posts and Telecommunications, China in 2017 and 2020, respectively. She is currently pursuing the Ph.D. degree with the Chongqing University of Posts and Telecommunications, China. Her research interests include analysis and processing of uncertain data, three-way decisions, fuzzy sets, granular computing, and rough sets.

Guoyin Wang received the BS, MS, and PhD degrees from Xi’an Jiaotong University, China in 1992, 1994, and 1996, respectively. Since 1996, he has been with the Chongqing University of Posts and Telecommunications, China, where he is currently a Professor, the Director of the Chongqing Key Laboratory of Computational Intelligence and the National International Scientific and Technological Cooperation Base of Big Data Intelligent Computing, and the Vice President of the University. He has authored 12 books, edited dozens of proceedings of international and national conferences, and has over 300 reviewed research publications. His current research interests include rough sets, granular computing, knowledge technology, data mining, neural network, and cognitive computing. Dr. Wang was the President of the International Rough Sets Society (IRSS) for the period 2014–2017. He is the Vice President of the Chinese Association for Artificial Intelligence.

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Liu, Q., Zhang, Q., Zhao, F. et al. Uncertain knowledge graph embedding: an effective method combining multi-relation and multi-path. Front. Comput. Sci. 18, 183311 (2024). https://doi.org/10.1007/s11704-023-2427-z

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