Abstract
Representation learning aims to represent the entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors by machine learning. The translation-based model is a typical representation learning method and has shown good predictive performance in large-scale knowledge graph. However, when modeling complex relations such as 1-N, N-1 and N-N, these models are not very effective. To solve the limitation of traditional learning model in modeling complex relations, a representation learning method based on dynamic step is proposed. Defining a dynamic step according to the different types of relations can significantly improve the efficiency of learning. The algorithm is used to solve the problem of single optimization goal, and the experimental results show that the dynamic step method can mainly improve the performance in the link prediction task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Z., Sun, M., et al.: Knowledge representation learning: a review. J. Comput. Res. Dev. 53(2), 247–261 (2016)
Needlakantan, A., Roth, B., et al.: Compositional vector space models for knowledge base completion. In: Proceedings of ACL, pp. 156–166. ACL, Stroudsburg (2015)
Dong, X., Gabrilovich, E., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of ACM SIGKDD, New York, pp. 601–610 (2014)
Socher, R., Chen, D., Manning, C.D., et al.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of NIPS, pp. 926–934. MIT Press, Cambridge (2013)
Bordes, A., Glorot, X., Weston, J., et al.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. Proceedings of ICLR (2013). arXiv:1301.3781
Bordes, A., Usunier, N., et al.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795. MIT Press, Cambridge (2013)
Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI, pp. 1112–1119. AAAI, Menlo Park (2014)
Lin, Y., Liu, Z., Sun, M., et al.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI. AAAI, Menlo Park (2015)
Ji, G., He, S., Xu, L., et al.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL, pp. 687–696. ACL, Stroudsburg (2015)
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of AAAI, pp. 985–991. AAAI, Phoenix (2016)
Xiao, H., Huang, M., et al.: TransA: an adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490 (2015)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Nos. 61572146, U1501252, U1711263), and the Natural Science Foundation of Guangxi Province (Nos. 2015GXNSFAAI39285).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Chang, L., Rao, G., Yochum, P., Luo, Y., Gu, T. (2018). Representation Learning for Knowledge Graph with Dynamic Step. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_29
Download citation
DOI: https://doi.org/10.1007/978-981-13-2203-7_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2202-0
Online ISBN: 978-981-13-2203-7
eBook Packages: Computer ScienceComputer Science (R0)