Abstract
In high connectivity knowledge graph, distance based knowledge graph embedding methods show promising performance on link prediction task, and are capable of encoding complex relations and key relation patterns. However, the existing methods fail to achieve excellent results in knowledge graph with poor context structure information. To mitigate this problem, we propose Bi2E, a bidirectional model based on subject-object feature spaces. To enhance the efficiency of data utilization and perceive more potential semantic links, we utilize the bidirectionality of relation to model from both forward and reverse directions. And Bi2E represents triples in the subject-object feature spaces, which enables it to capture richer feature information from rare data. In addition, Bi2E employs adaptive margin \(\gamma \) which makes embedded representation more flexible by only using a small amount of feature information. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of Bi2E. Moreover, we set a new state-of-the-art on two low connectivity knowledge graph benchmarks.
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References
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: 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 (2008)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165–180. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_11
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.-Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362. ACM (2016)
Yang, B., Mitchell, T.: Leveraging knowledge bases in LSTMS for improving machine reading. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1436–1446 (2017)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Sun, Z., Deng, Z.-H., Nie, J.-Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)
Teo, T.W., Choy, B.H.: In: Tan, O.S., Low, E.L., Tay, E.G., Yan, Y.K. (eds.) Singapore Math and Science Education Innovation. ETLPPSIP, vol. 1, pp. 43–59. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1357-9_3
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
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 (Volume 1: Long Papers), pp. 687–696 (2015)
Wang, Z., Zhang, J., Feng, J., Chen, Z.. Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. AAAI 16, 985–991 (2016)
Tang, Y., Huang, J., Wang, G., He, X., Zhou, B.: Orthogonal relation transforms with graph context modeling for knowledge graph embedding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2019)
Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. ICML 11, 809–816 (2011)
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080 (2016)
Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embedding. arXiv preprint arXiv:1904.10281 (2019)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems, pp. 4284–4295 (2018)
Wang, Y., Gemulla, R., Li, H.: On multi-relational link prediction with bilinear models (2018)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., Talukdar, P.: Interacte: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.: Composition-based multi-relational graph convolutional networks. In: 8th International Conference on Learning Representations (2020)
Akrami, F., Saeef, M.S., Zhang, Q., Hu, W., Li, C.: Realistic re-evaluation of knowledge graph completion methods: an experimental study. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (2020)
Mahdisoltani, F., Biega, J., Suchanek, F.M. Yago3: a knowledge base from multilingual wikipedias. In: CIDR (2015)
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 57–66 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Peng, X., Chen, G., Lin, C., Stevenson, M.: Highly efficient knowledge graph embedding learning with orthogonal procrustes analysis. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
Kadlec, R., Bajgar, O., Kleindienst, J.: Knowledge base completion: baselines strike back. arXiv preprint arXiv:1705.10744 (2017)
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Wang, Z., Li, X., Guo, Z. (2022). Bi2E: Bidirectional Knowledge Graph Embeddings Based on Subject-Object Feature Spaces. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2022. Lecture Notes in Computer Science, vol 13591. Springer, Cham. https://doi.org/10.1007/978-3-031-17834-4_1
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