Abstract
Representation Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimension space. Most methods concentrate on learning entities’ representations with structure information indicating the relations between entities (Trans- methods), while the utilization of entity multi-attribute information is insufficient for some scenarios, such as cold start issues or zero-shot problems. How to utilize the complex and diverse multi-attribute information for RL is still a challenging problem for enhancing knowledge graph embedding research. In this paper, we propose a novel RL model Duet Entity Representation Learning (DERL) for knowledge graphs, which takes advantage of entity multi-attribute information. Specifically, we devise a novel encoder Entity Attribute Encoder (EAE), which encodes both entity attribute types and values to generate the entities’ attribute-based representations. We further learn the entities’ representations with both structure information and multi-attribute information in DERL. We evaluate our method on two tasks: the knowledge graph completion task and the zero-shot task. Experimental results on real-world datasets show that our method outperforms other baselines on two downstream tasks by building effective representations for entities from their multi-attribute information. The source code of this paper can be obtained from https://anonymous.4open.science/r/DUET-adma2023/.
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
Similar content being viewed by others
References
Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of ICMD, pp. 1247–1250 (2008)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)
Che, F., Zhang, D., Tao, J., Niu, M., Zhao, B.: Parame: regarding neural network parameters as relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2774–2781 (2020)
Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., Ji, Y.: Hitter: Hierarchical transformers for knowledge graph embeddings. In: Proceedings of EMNLP, pp. 10395–10407 (2021)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs, pp. 4289–4300 (2018)
Lin, Y., Liu, Z., Sun, M.: Knowledge representation learning with entities, attributes and relations. In: Proceedings of IJCAI, pp. 2866–2872 (2016)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2181–2187 (2015)
Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual wikipedias. In: Proceedings of IDSR (2015)
Nguyen, D.Q., Vu, T., Nguyen, T.D., Phung, D.: Quatre: relation-aware quaternions for knowledge graph embeddings. CoRR abs/ arXiv: 2009.12517 (2020)
Shah, H., Villmow, J., Ulges, A., Schwanecke, U., Shafait, F.: An open-world extension to knowledge graph completion models. In: Proceedings of the AAAI, pp. 3044–3051 (2019)
Shen, Y., Li, Z., Wang, X., Li, J., Zhang, X.: Datatype-aware knowledge graph representation learning in hyperbolic space. In: Proceedings of CIKM, pp. 1630–1639 (2021)
Shi, B., Weninger, T.: Open-world knowledge graph completion. CoRR abs/ arXiv: 1711.03438 (2017)
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of IJCAI, pp. 4396–4402 (2018)
Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: Proceedings of ICLR (2019)
Trisedya, B.D., Qi, J., Zhang, R.: Entity alignment between knowledge graphs using attribute embeddings. In: Proceedings of AAAI, pp. 297–304 (2019)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of ICML, pp. 2071–2080 (2016)
Wang, S., et al.: Knowledge graph representation via hierarchical hyperbolic neural graph embedding. In: 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15–18 December 2021, pp. 540–549. IEEE (2021). https://doi.org/10.1109/BigData52589.2021.9671651
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI, pp. 1112–1119 (2014)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of AAAI, pp. 2659–2665 (2016)
Xie, R., Liu, Z., Luan, H., Sun, M.: Image-embodied knowledge representation learning. In: Proceedings of IJCAI, pp. 3140–3146 (2017)
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.) In Proceedings of ICLR (2015)
Yang, K., Liu, S., Zhao, J., Wang, Y., Xie, B.: COTSAE: co-training of structure and attribute embeddings for entity alignment. In: Proceedings of AAAI, pp. 3025–3032 (2020)
Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., Li, X.: Neural generative question answering. In: Proceedings of IJCAI, pp. 2972–2978 (2016)
Zhang, F., Wang, X., Li, Z., Li, J.: Transrhs: A representation learning method for knowledge graphs with relation hierarchical structure. In: Proceedings of IJCAI, pp. 2987–2993 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Y., Zhang, Y., Yang, Y., Xu, H., Yue, L. (2023). Duet Representation Learning with Entity Multi-attribute Information in Knowledge Graphs. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-46664-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46663-2
Online ISBN: 978-3-031-46664-9
eBook Packages: Computer ScienceComputer Science (R0)