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Single Source Path-Based Graph Neural Network for Inductive Knowledge Graph Reasoning

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence (CCKS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1923))

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Abstract

In this paper, we introduce our solution for the inductive knowledge graph reasoning task organized by the 2023 China Conference on Knowledge Graph and Semantic Computing (CCKS 2023). Specifically, this inductive knowledge graph reasoning task has two main challenges, namely 1) How to predict the entities that are not in the training set, and 2) How to train and reason more efficiently. To deal with these challenges, we adapt the Neural Bellman-Ford Networks (NBFNet) with grid search strategy for achieving the best performance. Along this line, we further refine this solution with ensemble learning and post-processing. Extensive experiments have demonstrated the effectiveness of our solution, which won the first place in the competition. Code is publicly available at https://github.com/smart-lty/CCKS-2023-Task2

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References

  1. Zhang, Z., et al.: ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 2019, pp. 1441–1451. Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1139. https://aclanthology.org/P19-1139

  2. Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, pp. 417–426. Association for Computing Machinery (2018). ISBN 9781450360142. https://doi.org/10.1145/3269206.3271739

  3. Huang, X., et al.: Knowledge graph embedding based question answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, pp. 105–113. Association for Computing Machinery (2019). ISBN 9781450359405. https://doi.org/10.1145/3289600.3290956

  4. Bollacker, K., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, Canada, pp. 1247–1250. Association for Computing Machinery (2008). ISBN 9781605581026. https://doi.org/10.1145/1376616.1376746

  5. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014). ISSN 0001-0782. https://doi.org/10.1145/2629489

  6. Mahdisoltani, F., Biega, J.A., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: Conference on Innovative Data Systems Research (2015). https://api.semanticscholar.org/CorpusID:6611164

  7. Sun, Z., et al.: RotatE: knowledge graph embedding by relational rotation in complex space. arXiv:1902.10197 [cs.LG] (2019)

  8. Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38

    Chapter  Google Scholar 

  9. Galkin, M., Berrendorf, M., Hoyt, C.T.: An open challenge for inductive link prediction on knowledge graphs (2022)

    Google Scholar 

  10. Teru, K.K., Denis, E.G., Hamilton, W.L.: Inductive relation prediction by subgraph reasoning. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020. JMLR.org (2020)

    Google Scholar 

  11. Chen, J., et al.: Topology-aware correlations between relations for inductive link prediction in knowledge graphs. Proc. AAAI Conf. Artif. Intell. 35(7), 6271–6278 (2021). https://doi.org/10.1609/aaai.v35i7.16779. https://ojs.aaai.org/index.php/AAAI/article/view/16779.

  12. Galárraga, L., et al.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707–730 (2015). ISSN 1066-8888. https://doi.org/10.1007/s00778-015-0394-1

  13. Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, Long Beach, California, USA, pp. 2316–2325. Curran Associates Inc. (2017). ISBN 9781510860964

    Google Scholar 

  14. Kok, S., Domingos, P.: Statistical predicate invention. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, Corvalis, Oregon, USA, pp. 433–440. Association for Computing Machinery (2007). ISBN 9781595937933. https://doi.org/10.1145/1273496.1273551

  15. Wang, W.Y., Mazaitis, K., Cohen, W.W.: Structure learning via parameter learning. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, pp. 1199–1208. Association for Computing Machinery (2014). ISBN 9781450325981. https://doi.org/10.1145/2661829.2662022

  16. Mai, S., et al.: Communicative message passing for inductive relation reasoning. arXiv:2012.08911 [cs.AI] (2021)

  17. Rameshkumar, K., Sambath, M., Ravi, S.: Relevant association rule mining from medical dataset using new irrelevant rule elimination technique. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), ICICES 2013, pp. 300–304 (2013). https://doi.org/10.1109/ICICES.2013.6508351

  18. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  19. Brin, S.: The PageRank citation ranking: bringing order to the web. In: Proceedings of ASIS 1998, vol. 98, pp. 161–172 (1998)

    Google Scholar 

  20. Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556–559 (2003)

    Google Scholar 

  21. Baras, J., Theodorakopoulos, G.: Path Problems in Networks. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-79983-9

    Book  MATH  Google Scholar 

  22. Bellman, R.: On a routing problem. Q. Appl. Math. 16(1), 87–90 (1958)

    Article  MATH  Google Scholar 

  23. Galárraga, L.A., et al.: 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 (2013)

    Google Scholar 

  24. Sadeghian, A., et al.: DRUM: end-to-end differentiable rule mining on knowledge graphs. arXiv:1911.00055 [cs.LG] (2019)

  25. Das, R., et al.: Chains of reasoning over entities, relations, and text using recurrent neural networks. arXiv:1607.01426 [cs.CL] (2017)

  26. Lao, N., et al.: Efficient relational learning with hidden variable detection. In: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1, NIPS 2010, Vancouver, British Columbia, Canada, pp. 1234–1242. Curran Associates Inc. (2010)

    Google Scholar 

  27. Zhu, Z., et al.: Neural Bellman-Ford networks: a general graph neural network framework for link prediction. arXiv:2106.06935 [cs.LG] (2022)

  28. Bordes, A., et al.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2013, Lake Tahoe, Nevada, pp. 2787–2795. Curran Associates Inc. (2013)

    Google Scholar 

  29. Yang, B., et al.: Embedding entities and relations for learning and inference in knowledge bases. arXiv:1412.6575 [cs.CL] (2015)

  30. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. arXiv:1802.09691 [cs.LG] (2018)

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Authors and Affiliations

  1. School of Data Science, University of Science and Technology of China, Hefei, China

    Deqiang Huang, Guoqing Zhao, Tianyu Liu, Tong Xu & Enhong Chen

Authors
  1. Deqiang Huang
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  2. Guoqing Zhao
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  3. Tianyu Liu
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  4. Tong Xu
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  5. Enhong Chen
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Corresponding author

Correspondence to Tong Xu .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    Haofen Wang

  2. Chinese Academy of Sciences, Beijing, China

    Xianpei Han

  3. Harbin Institute of Technology, Harbin, China

    Ming Liu

  4. Nanjing University, Nanjing, China

    Gong Cheng

  5. University of South China, Hengyang, China

    Yongbin Liu

  6. Zhejiang University, Hangzhou, China

    Ningyu Zhang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Huang, D., Zhao, G., Liu, T., Xu, T., Chen, E. (2023). Single Source Path-Based Graph Neural Network for Inductive Knowledge Graph Reasoning. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_22

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  • DOI: https://doi.org/10.1007/978-981-99-7224-1_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7223-4

  • Online ISBN: 978-981-99-7224-1

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