Abstract
Recent years have witnessed great advance of representation learning (RL) based models for the knowledge graph relation prediction task. Most existing approaches represent graph nodes as vectors in a low-dimensional embedding space, ignoring the entity behavior preference and the beneficial semantic interactions in the real-world graphs. To address this challenge, this paper proposes a novel relation prediction model based on source-entity behavior preference modeling, which represents each source-entity as a heterogeneous graph released from their structure and semantic perspectives to better capture the behavior relatedness. Especially, a heterogeneous graph pooling method is leveraged for learning source-entity behavior embedding representation from this personalized heterogeneous graph. In our comprehensive experiments, we evaluate our model on real-world graphs, and the results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods on benchmark datasets for the relation prediction and knowledge graph completion task.
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Notes
- 1.
\(T=3\) for \(\mathcal {G}^{0}\) in our scene as shown in different colors in Fig. 1. However, due to the scalability of the proposed architecture, when more node types are introduced into the heterogeneous graph, our architecture is still at work.
References
An, B., Chen, B., Han, X., Sun, L.: Accurate text-enhanced knowledge graph representation learning. In: NAACL-HLT (2018)
Arora, S.: A survey on graph neural networks for knowledge graph completion. arXiv: abs/2007.12374 (2020)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: Joint learning of words and meaning representations for open-text semantic parsing. In: AISTATS (2012)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS 2013, pp. 2787–2795 (2013)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI 2011, pp. 301–306 (2011)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: COMPSTAT (2010)
Huang, H., Wang, Y., Feng, C., Liu, Z., Zhou, Q.: Leveraging conceptualization for short-text embedding. IEEE Trans. Knowl. Data Eng. 30(7), 1282–1295 (2018)
Huxue, R., Huang, J., Wang, J., Li, Y.: Learning gaussian hierarchy embedding for relation prediction in knowledge graph. J. Phys. Conf. Ser. 1748, 032039 (2021)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2015)
Lin, Y., Liu, Z., Luan, H.B., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: EMNLP (2015)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI 2015, pp. 2181–2187 (2015)
Ma, S., Ding, J., Jia, W., Wang, K., Guo, M.: TransT: type-based multiple embedding representations for knowledge graph completion. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 717–733 (2017)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1955–1961 (2016)
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: International Conference on International Conference on Machine Learning, pp. 809–816 (2011)
Song, Y., Wang, S., Wang, H.: Open domain short text conceptualization: a generative + descriptive modeling approach. In: International Conference on Artificial Intelligence, pp. 3820–3826 (2015)
Trouillon, T., Welbl, J., Riedel, S., Éric Gaussier, Bouchard, G.: Complex embeddings for simple link prediction (2016)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio’, P., Bengio, Y.: Graph attention networks. arXiv:abs/1710.10903 (2018)
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)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Wang, Y., yan Huang, H., Feng, C.: Query expansion with local conceptual word embeddings in microblog retrieval. IEEE Trans. Knowl. Data Eng. 33, 1737–1749 (2021)
Wang, Y., Liu, Y., Zhang, H., Xie, H.: Leveraging lexical semantic information for learning concept-based multiple embedding representations for knowledge graph completion. In: APWeb/WAIM (2019)
Wang, Y., Zhang, H.: HARP: a novel hierarchical attention model for relation prediction. ACM Trans. Knowl. Discov. Data 15, 17:1–17:22 (2021)
Wang, Y., Zhang, H.: Introducing graph neural networks for few-shot relation prediction in knowledge graph completion task. In: KSEM (2021)
Wang, Y., Zhang, H., Li, Y., Xie, H.: Simplified representation learning model based on parameter-sharing for knowledge graph completion. In: CCIR (2019)
Wang, Y., Zhang, H., Liu, Z., Zhou, Q.: Hierarchical concept-driven language model. ACM Trans. Knowl. Disc. Data (TKDD) 15, 1–22 (2021)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI 2014, pp. 1112–1119 (2014)
Wang, Z., Zhao, K., Wang, H., Meng, X., Wen, J.R.: Query understanding through knowledge-based conceptualization. In: International Conference on Artificial Intelligence, pp. 3264–3270 (2015)
Wu, C., Wu, F., Huang, Y., Xie, X.: User-as-graph: User modeling with heterogeneous graph pooling for news recommendation. In: IJCAI (2021)
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 481–492 (2012)
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: ACM SIGMOD International Conference on Management of Data, pp. 481–492 (2012)
Xiao, H., Huang, M., Zhu, X.: TransG : a generative model for knowledge graph embedding. In: Meeting of the Association for Computational Linguistics, pp. 2316–2325 (2016)
Xie, R., Liu, Z., Jia, J.J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: AAAI (2016)
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. arXiv:abs/1806.08804 (2018)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858 (2020)
Zhao, Y., et al.: Time-aware path reasoning on knowledge graph for recommendation. ACM Trans. Inf. Syst. (TOIS) (2022). https://doi.org/10.1145/3531267
Acknowledgements
We thank anonymous reviewers for valuable comments. This work is funded by: (i) the National Natural Science Foundation of China (No. 62106243, U19B2026).
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Wang, Y., Zhu, X., Zhang, H. (2022). Relation Prediction Based on Source-Entity Behavior Preference Modeling via Heterogeneous Graph Pooling. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_33
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