Abstract
As an effective and efficient approach for describing real-world entities with relations, Knowledge graph (KG) technology has received a lot of attention in recent years. KG embedding technology becomes one hot spot in KG research in terms of its efficiency in KG completion, relationship extraction, entity classification and resolution, etc. KG embedding is to embed KG entities and relations as vectors into the continuous spaces, aims at simplifying the operation, also retaining the inherent structure of the KG. In this paper, we focus on the progress of KG embedding in recent these years and summarize the latest embedding methods systematically into a review. We classify these embedding methods into two broad categories based on the type of information in the KG used in this review. We started at introducing models that only use the fact information in the KG followed by briefly review of previous models, focusing on the new models and the improved models based on the previous models. We describe the core ideas, implementation methods, the advantages and disadvantages of these technologies. Then, models incorporate additional information, including entity types, relational paths, textual descriptions, and the use of logical rules will be also included.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nayyeri, M., Zhou, X., Vahdati, S., Yazdi, H.S., Lehmann, J.: Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function, pp. 1–7 (2019)
Zhu, J.-Z., Jia, Y.-T., Xu, J., Qiao, J.-Z., Cheng, X.-Q.: Modeling the correlations of relations for knowledge graph embedding. J. Comput. Sci. Technol. 33(2), 323–334 (2018). https://doi.org/10.1007/s11390-018-1821-8
Ebisu, T., Ichise, R.: TorusE: knowledge graph embedding on a lie group. In: 32nd AAAI Conference Artificial Intelligence, AAAI 2018, pp. 1819–1826 (2018)
Niu, B., Huang, Y.: An improved method for web text affective cognition computing based on knowledge graph. Comput. Mater. Contin. 59, 1–14 (2019). https://doi.org/10.32604/cmc.2019.06032
Li, D., Wu, H., Gao, J., Liu, Z., Li, L., Zheng, Z.: Uncertain knowledge reasoning based on the fuzzy multi entity Bayesian networks. Comput. Mater. Contin. 61, 301–321 (2019). https://doi.org/10.32604/cmc.2019.05953
Lu, W., et al.: Graph-based Chinese word sense disambiguation with multi-knowledge integration. Comput. Mater. Contin. 61, 197–212 (2019). https://doi.org/10.32604/cmc.2019.06068
Kolyvakis, P., Kalousis, A., Kiritsis, D.: HyperKG: hyperbolic knowledge graph embeddings for knowledge base completion (2019)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29, 2724–2743 (2017). https://doi.org/10.1109/TKDE.2017.2754499
Kishimoto, K., Hayashi, K., Akai, G., Shimbo, M., Komatani, K.: Binarized knowledge graph embeddings. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 181–196. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15712-8_12
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing System, pp. 4284–4295 (2018)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: 32nd AAAI Conference on Artificial Intelligence AAAI, pp. 1811–1818 (2018)
Ding, B., Wang, Q., Wang, B., Guo, L.: Improving knowledge graph embedding using simple constraints. In: ACL 2018 - 56th Annual Meeting Association Computer Linguistic Proceedings Conference, Long Paper, vol. 1, pp. 110–121 (2018). https://doi.org/10.18653/v1/p18-1011
Fatemi, B., Ravanbakhsh, S., Poole, D.: Improved knowledge graph embedding using background taxonomic information. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3526–3533 (2019)
Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embedding. In: Advances in Neural Information Processing Systems, pp. 1–12 (2019)
Cai, C.: Group Representation Theory for Knowledge Graph Embedding (2019)
Yuan, J., Gao, N., Xiang, J.: TransGate: knowledge graph embedding with shared gate structure. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3100–3107 (2019). https://doi.org/10.1609/aaai.v33i01.33013100
Zhu, Q., Zhou, X., Zhang, P., Shi, Y.: A neural translating general hyperplane for knowledge graph embedding. J. Comput. Sci. 30, 108–117 (2019). https://doi.org/10.1016/j.jocs.2018.11.004
Lv, X., Hou, L., Li, J., Liu, Z.: Differentiating Concepts and Instances for Knowledge Graph Embedding, pp. 1971–1979 (2019). https://doi.org/10.18653/v1/d18-1222
Rahman, M.M., Takasu, A.: Knowledge graph embedding via entities’ type mapping matrix. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11303, pp. 114–125. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04182-3_11
Jia, Y., Wang, Y., Jin, X., Cheng, X.: Path-specific knowledge graph embedding. Knowl.-Based Syst. 151, 37–44 (2018). https://doi.org/10.1016/j.knosys.2018.03.020
Ma, L., Sun, P., Lin, Z., Wang, H.: Composing Knowledge Graph Embeddings via Word Embeddings (2019)
Xiao, H., Chen, Y., Shi, X.: Knowledge graph embedding based on multi-view clustering framework. IEEE Trans. Knowl. Data Eng., 1 (2019). https://doi.org/10.1109/tkde.2019.2931548
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 4816–4823 (2018)
Nguyen, D.Q., Nguyen, T.D., Phung, D.: Relational Memory-based Knowledge Graph Embedding (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Duan, C., You, H., Cai, Z., Zhang, Z. (2020). Research Progress of Knowledge Graph Embedding. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-8083-3_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8082-6
Online ISBN: 978-981-15-8083-3
eBook Packages: Computer ScienceComputer Science (R0)