Abstract
Temporal knowledge graph completion (TKGC) is an important research task due to the incompleteness of temporal knowledge graphs. However, existing TKGC models face the following two issues: 1) these models cannot be directly applied to few-shot scenario where most relations have only few quadruples and new relations will be added; 2) these models cannot fully exploit the dynamic time and relation properties to generate discriminative embeddings of entities. In this paper, we propose a temporal-relational matching network, namely TR-Match, for few-shot temporal knowledge graph completion. Specifically, we design a multi-scale time-relation attention encoder to adaptively capture local and global information based on time and relation to tackle the dynamic properties problem. Then, we build a new matching processor to tackle the few-shot problem by mapping the query to few support quadruples in a relation-agnostic manner. Finally, we construct three new datasets for few-shot TKGC task based on benchmark datasets. Extensive experimental results demonstrate the superiority of our model over the state-of-the-art baselines.
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References
Bai, L., Zhang, M., Zhang, H., Zhang, H.: FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism. In: World Wide Web, pp. 1–28 (2022)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NeurIPS, pp. 2787–2795 (2013)
Boschee, E., Lautenschlager, J., O’Brien, S., Shellman, S., Starz, J., Ward, M.: ICEWS Coded Event Data (2015). https://doi.org/10.7910/DVN/28075
Chen, M., Zhang, W., Zhang, W., Chen, Q., Chen, H.: Meta relational learning for few-shot link prediction in knowledge graphs. In: EMNLP-IJCNLP, pp. 4217–4226 (2019)
Goel, R., Kazemi, S.M., Brubaker, M.A., Poupart, P.: Diachronic embedding for temporal knowledge graph completion. In: AAAI, pp. 3988–3995 (2020)
Jeon, I., Papalexakis, E.E., Faloutsos, C., Sael, L., Kang, U.: Mining billion-scale tensors: Algorithms and discoveries. VLDB J. 25(4), 519–544 (2016). https://doi.org/10.1007/s00778-016-0427-4
Jiang, Z., Gao, J., Lv, X.: Metap: Meta pattern learning for one-shot knowledge graph completion. In: SIGIR, pp. 2232–2236 (2021)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. NeurIPS, pp. 4289–4300 (2018)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR (2015)
Lacroix, T., Obozinski, G., Usunier, N.: Tensor decompositions for temporal knowledge base completion. In: ICLR (2019)
Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. WWW, pp. 1771–1776 (2018)
Liu, Z., Xiong, C., Sun, M., Liu, Z.: Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval. In: ACL, pp. 2395–2405 (2018)
Niu, G., et al.: Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion. In: SIGIR, pp. 213–222 (2021)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)
Sheng, J., et al.: Adaptive attentional network for few-shot knowledge graph completion. In: EMNLP, pp. 1681–1691 (2020)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. NeurIPS, pp. 3637–3645 (2016)
Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.S.: Explainable reasoning over knowledge graphs for recommendation. In: AAAI, pp. 5329–5336 (2019)
Xiong, W., Yu, M., Chang, S., Guo, X., Wang, W.Y.: One-shot relational learning for knowledge graphs. In: EMNLP, pp. 1980–1990 (2018)
Xu, C., Chen, Y.Y., Nayyeri, M., Lehmann, J.: Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings. In: NAACL, pp. 2569–2578 (2021)
Xu, C., Nayyeri, M., Alkhoury, F., Yazdi, H.S., Lehmann, J.: Tero: A time-aware knowledge graph embedding via temporal rotation. In: COLING, pp. 1583–1593 (2020)
Xu, C., Nayyeri, M., Alkhoury, F., Yazdi, H., Lehmann, J.: Temporal knowledge graph completion based on time series gaussian embedding. In: ISWC, pp. 654–671 (2020)
Xu, D., Ruan, C., Körpeoglu, E., Kumar, S., Achan, K.: Inductive representation learning on temporal graphs. ArXiv (2020)
Zhang, C., Yao, H., Huang, C., Jiang, M., Li, Z.J., Chawla, N.: Few-shot knowledge graph completion. In: AAAI, pp. 3041–3048 (2020)
Zhang, Y., Dai, H., Kozareva, Z., Smola, A.J., Song, L.: Variational reasoning for question answering with knowledge graph. In: AAAI, pp. 1–8 (2018)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China: 61976051, U19A2067, and the Major Key Project of PCL: PCL2022A03
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Gong, X., Qin, J., Chai, H., Ding, Y., Jia, Y., Liao, Q. (2023). Temporal-Relational Matching Network for Few-Shot Temporal Knowledge Graph Completion. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_52
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