Abstract
Knowledge graphs (KGs) are large scale multi-relational directed graph, which comprise a large amount of triplets. Embedding knowledge graphs into continuous vector space is an essential problem in knowledge extraction. Many existing knowledge graph embedding methods focus on learning rich features from entities and relations with increasingly complex feature engineering. However, they pay little attention on the order information of triplets. As a result, current methods could not capture the inherent directional property of KGs fully. In this paper, we explore knowledge graphs embedding from an ingenious perspective, viewing a triplet as a fixed length sequence. Based on this idea, we propose a novel recurrent knowledge graph embedding method RKGE. It uses an order keeping concatenate operation and a shared sigmoid layer to capture order information and discriminate fine-grained relation-related information. We evaluate our method on knowledge graph completion on benchmark data sets. Extensive experiments show that our approach outperforms state-of-the-art baselines significantly with relatively much lower space complexity. Especially on sparse KGs, RKGE achieves a 86.5% improvement at Hits@1 on FB15K-237. The outstanding results demonstrate that the order information of triplets is highly beneficial for knowledge graph embedding.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, pp. 1247–1250 (2008)
Bordes, A., Usunier, N., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)
Cai, L., Wang, W.Y.: KBGAN: adversarial learning for knowledge graph embeddings. In: Proceedings of NAACL (2018)
Dai, Q.N., Tu, D.N., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of NAACL (2018)
Dettmers, T., Pasquale, M., Pontus, S., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of AAAI, pp. 1811–1818 (2018)
Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of SIGKDD, pp. 601–610 (2014)
Garciaduran, A., Bordes, A., Usunier, N.: Composing relationships with translations. In: Proceedings of EMNLP, pp. 286–290 (2015)
Garciaduran, A., Niepert, M.: KBLRN: end-to-end learning of knowledge base representations with latent, relational, and numerical features. In: Proceedings of UAI (2017)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: Proceedings of AAAI (2018)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL, pp. 687–696 (2015)
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of AAAI, pp. 985–991 (2016)
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: Proceedings of EMNLP, pp. 705–714 (2015)
Lin, Y., Liu, Z., Zhu, X., Zhu, X., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2181–2187 (2015)
Lipton, Z.C.: A critical review of recurrent neural networks for sequence learning. CoRR abs/1506.00019 (2015). http://arxiv.org/abs/1506.00019
Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. In: Proceedings of ICML (2017)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Qian, W., Fu, C., Zhu, Y., Cai, D., He, X.: Translating embeddings for knowledge graph completion with relation attention mechanism. In: Proceedings of IJCAI, pp. 4286–4292 (2018)
Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of NIPS, pp. 926–934 (2013)
Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of EMNLP, pp. 1631–1642 (2013)
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of CVCS (2015)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of ICML, vol. 48, pp. 2071–2080 (2016)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI, pp. 1112–1119 (2014)
Xu, H., Yankai, L., Ruobing, X., Zhiyuan, L., Maosong, S.: OpenKE: an open-source framework for knowledge embedding (2017). http://openke.thunlp.org/home
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of ICLR (2015)
Acknowledgement
This work is supported by the National Key Research and Development Program of China, and National Natural Science Foundation of China (No. U163620068).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, J., Gao, N., Xiang, J., Tu, C., Ge, J. (2019). Knowledge Graph Embedding with Order Information of Triplets. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-16142-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16141-5
Online ISBN: 978-3-030-16142-2
eBook Packages: Computer ScienceComputer Science (R0)