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LoGNet: Local and Global Triple Embedding Network

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The Semantic Web – ISWC 2022 (ISWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13489))

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Abstract

This paper introduces an end-to-end learning framework called LoGNet (Local and Global Triple Embedding Network) for triple-centric tasks in knowledge graphs (KGs). LoGNet is based on graph neural networks (GNNs) and combines local and global triple embedding information. Local triple embeddings are learned by treating triples as sequences. Global triple embeddings are learned by operating on the feature triple line graph \(\mathcal {G}_{L}\) of a knowledge graph \(\mathcal {G}\). The nodes of \(\mathcal {G}_{L}\) are the triples of \(\mathcal {G}\), edges are inserted according to subjects/objects shared by triples, and node and edge features are derived from the triples of \(\mathcal {G}\). LoGNet brings a refreshing triple-centric perspective in learning from KGs and is flexible enough to adapt to various downstream tasks. We discuss concrete use-cases in triple classification and anomalous predicate detection. An experimental evaluation shows that LoGNet brings better performance than the state-of-the-art.

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Notes

  1. 1.

    https://dbpedia.org/page/Invictus_(film).

  2. 2.

    https://dbpedia.org/page/Americans.

  3. 3.

    https://github.com/giuseppepirro/lognet.

  4. 4.

    https://www.dgl.ai.

  5. 5.

    http://yago-knowledge.org/.

  6. 6.

    With RotatE and TransE we obtained inferior results.

References

  1. Abboud, R., Ceylan, İ.İ., Grohe, M., Lukasiewicz, T.: The surprising power of graph neural networks with random node initialization. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event/Montreal, Canada, 19–27 August 2021, pp. 2112–2118 (2021)

    Google Scholar 

  2. Belth, C., Zheng, X., Vreeken, J., Koutra, D.: What is normal, what is strange, and what is missing in a knowledge graph: unified characterization via inductive summarization. In: Proceedings of the Web Conference 2020, pp. 1115–1126 (2020)

    Google Scholar 

  3. Bianconi, G., Pin, P., Marsili, M.: Assessing the relevance of node features for network structure. Proc. Natl. Acad. Sci. 106(28), 11433–11438 (2009)

    Article  Google Scholar 

  4. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 2787–2795 (2013)

    Google Scholar 

  5. Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)

    Article  Google Scholar 

  6. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 5 (2010)

    Google Scholar 

  7. Chekol, M.W., Pirrò, G.: Refining node embeddings via semantic proximity. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12506, pp. 74–91. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62419-4_5

    Chapter  Google Scholar 

  8. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)

    Google Scholar 

  9. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Deep Learning and Representation Learning Workshop (2014)

    Google Scholar 

  10. Darari, F., Nutt, W., Pirrò, G., Razniewski, S.: Completeness management for RDF data sources. ACM Trans. Web (TWEB) 12(3), 1–53 (2018)

    Article  Google Scholar 

  11. Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference, pp. 1811–1818 (2018)

    Google Scholar 

  12. Dong, X., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of International Conference on Information and Knowledge Management, pp. 135–144 (2017)

    Google Scholar 

  13. Fionda, V., Pirrò, G.: Learning triple embeddings from knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3874–3881 (2020)

    Google Scholar 

  14. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 855–864. ACM (2016)

    Google Scholar 

  15. Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4–9 December 2017, Long Beach, CA, USA, pp. 1024–1034 (2017)

    Google Scholar 

  16. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  17. Hussein, R., Yang, D., Cudré-Mauroux, P.: Are meta-paths necessary?: revisiting heterogeneous graph embeddings. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22–26 October 2018, pp. 437–446. ACM (2018)

    Google Scholar 

  18. Jia, S., Xiang, Y., Chen, X., Wang, K.: Triple trustworthiness measurement for knowledge graph. In: Liu, L., et al. (eds.) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13–17 May 2019, pp. 2865–2871. ACM (2019)

    Google Scholar 

  19. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)

    Google Scholar 

  20. Liang, J., Xiao, Y., Zhang, Y., Hwang, S.W., Wang, H.: Graph-based wrong ISA relation detection in a large-scarsescale lexical taxonomy. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 1178–1184 (2017)

    Google Scholar 

  21. Melo, A., Paulheim, H.: Detection of relation assertion errors in knowledge graphs. In: Proceedings of the Knowledge Capture Conference, pp. 1–8 (2017)

    Google Scholar 

  22. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of International Conference on Neural Information Processing, pp. 3111–3119 (2013)

    Google Scholar 

  23. Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention-based embeddings for relation prediction in knowledge graphs. In: 57th Annual Meeting of the Association for Computational Linguistics, pp. 4710–4723 (2019)

    Google Scholar 

  24. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA, 24–27 August 2014, pp. 701–710 (2014)

    Google Scholar 

  25. Pirrò, G.: Building relatedness explanations from knowledge graphs. Semant. Web 10(6), 963–990 (2019)

    Article  Google Scholar 

  26. Pirrò, G.: Fact-checking via path embedding and aggregation. In: Joint Proceedings of Workshops AI4LEGAL2020, NLIWOD, PROFILES 2020, QuWeDa 2020 and SEMIFORM2020 Colocated with the 19th International Semantic Web Conference (ISWC 2020), Virtual Conference, November 2020. CEUR Workshop Proceedings, vol. 2722, pp. 149–158. CEUR-WS.org (2020)

    Google Scholar 

  27. Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Proceedings of International Semantic Web Conference, pp. 498–514 (2016)

    Google Scholar 

  28. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2009)

    Article  Google Scholar 

  29. Shiralkar, P., Flammini, A., Menczer, F., Ciampaglia, G.L.: Finding streams in knowledge graphs to support fact checking. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 859–864. IEEE (2017)

    Google Scholar 

  30. Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: RotatE: knowledge graph embedding by relational rotation in complex space. In: Proceedings of International Conference on Learning Representations (2019)

    Google Scholar 

  31. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of International Conference on Machine Learning, pp. 2071–2080 (2016)

    Google Scholar 

  32. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of International Conference on Learning Representations (2017)

    Google Scholar 

  33. Wang, X., Lu, Y., Shi, C., Wang, R., Cui, P., Mou, S.: Dynamic heterogeneous information network embedding with meta-path based proximity. IEEE Trans. Knowl. Data Eng. 34(3), 1117–1132 (2022)

    Article  Google Scholar 

  34. West, D.B., et al.: Introduction to Graph Theory, vol. 2. Prentice Hall, Hoboken (1996)

    MATH  Google Scholar 

  35. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May 2019. OpenReview.net (2019)

    Google Scholar 

  36. Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

  37. You, J., Ying, R., Leskovec, J.: Position-aware graph neural networks. In: International Conference on Machine Learning, pp. 7134–7143. PMLR (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

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  1. Giuseppe Pirrò
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Correspondence to Giuseppe Pirrò .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Ulrike Sattler

  2. University of Chile, Santiago, Chile

    Aidan Hogan

  3. University of Cape Town, Cape Town, South Africa

    Maria Keet

  4. University of Bologna, Bologna, Italy

    Valentina Presutti

  5. Universidade Federal do Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  6. National Institute of Informatics, Tokyo, Japan

    Hideaki Takeda

  7. Orange, Belfort, France

    Pierre Monnin

  8. Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

  9. University of Bari, Bari, Italy

    Claudia d’Amato

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Pirrò, G. (2022). LoGNet: Local and Global Triple Embedding Network. In: Sattler, U., et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-19433-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19432-0

  • Online ISBN: 978-3-031-19433-7

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