Abstract
Few-Shot Knowledge Graph Completion (FSKGC) aims to predict new facts for relations with only a few observed instances in Knowledge Graph. Existing FSKGC models mostly tackle this problem by devising an effective graph encoder to enhance entity representations with features from their directed neighbors. However, due to the sparsity and entity diversity of large-scale KG, these approaches fail to generate reliable embeddings for solitary entities, which only have an extremely limited number of neighbors in KG. In this paper, we attempt to mitigate this issue by modeling semantic correlations between entities within an FSKGC task and propose our model YANA (You Are Not Alone). Specifically, YANA introduces four novel abstract relations to represent inner- and cross- pair entity correlations and construct a Local Pattern Graph (LPG) from the entities. Based on LPG, YANA devises a Highway R-GCN to capture hidden dependencies of entities. Moreover, a query-aware gating mechanism is proposed to combine topology signals from LPG and semantic information learned from entity’s directed neighbors with a heterogeneous graph attention network. Experiments show that YANA outperforms the prevailing FSKGC models on two datasets, and the ablation studies prove the effectiveness of Local Pattern Graph design.
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
Notes
- 1.
There will be eight edges for two pairs. We omit the other six samples for brevity.
- 2.
Due to paper length restrictions, we omit the details of transformer and refer readers to the origin paper [18].
References
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)
Chen, M., Zhang, W., Zhang, W., Chen, Q., Chen, H.: Meta relational learning for few-shot link prediction in knowledge graphs. In: EMNLP, pp. 4216–4225. Association for Computational Linguistics (2019)
Dai, D., Zheng, H., Sui, Z., Chang, B.: Incorporating connections beyond knowledge embeddings: a plug-and-play module to enhance commonsense reasoning in machine reading comprehension. arXiv:2103.14443 (2021)
Gao, T., Han, X., Liu, Z., Sun, M.: Hybrid attention-based prototypical networks for noisy few-shot relation classification. In: AAAI (2019)
Hu, F., Lakdawala, S., Hao, Q., Qiu, M.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf. Technol. Biomed. 13, 656–663 (2009)
Ioannidis, V.N., Zheng, D., Karypis, G.: Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. arXiv:2007.10261 (2020)
Li, Y., Song, Y., Jia, L., Gao, S., Li, Q., Qiu, M.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning. IEEE Trans. Industr. Inf. 17, 2833–2841 (2021)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML, pp. 807–814. Omnipress (2010)
Niu, G., et al.: Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Qiu, M., Lu, J.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22, 4560–4569 (2021)
Satorras, V.G., Bruna, J.: Few-shot learning with graph neural networks. arXiv:1711.04043 (2018)
Saxena, A., Tripathi, A., Talukdar, P.P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: ACL (2020)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Sheng, J., et al.: Adaptive attentional network for few-shot knowledge graph completion. In: EMNLP, pp. 1681–1691. Association for Computational Linguistics (2020)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv:1505.00387 (2015)
Tian, A., Zhang, C., Rang, M., Yang, X., Zhan, Z.: RA-GCN: relational aggregation graph convolutional network for knowledge graph completion. In: Proceedings of the 2020 12th International Conference on Machine Learning and Computing (2020)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, vol. 48, pp. 2071–2080. JMLR.org (2016)
Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS, pp. 3630–3638 (2016)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
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., Zhang, H.: Introducing graph neural networks for few-shot relation prediction in knowledge graph completion task. In: KSEM (2021)
Xiong, W., Yu, M., Chang, S., Guo, X., Wang, W.Y.: One-shot relational learning for knowledge graphs. In: EMNLP, pp. 1980–1990. Association for Computational Linguistics, Brussels, Belgium, October–November 2018
Zhang, C., Yao, H., Huang, C., Jiang, M., Li, Z., Chawla, N.: Few-shot knowledge graph completion. In: AAAI, vol. 34, pp. 3041–3048, April 2020
Acknowledgements
This work is supported by Beijing Nova Program of Science and Technology (Grant No. Z191100001119031), National Natural Science Foundation of China (Grant No. U21A20468), Guangxi Key Laboratory of Cryptography and Information Security (Grant No. GCIS202111), The Open Program of Zhejiang Lab (Grant No. 2019KE0AB03), and Zhejiang Lab (Grant No. 2021PD0AB02). Yi Liang is supported by BUPT Excellent Ph.D. Students Foundation under grant CX2019136. Shuai Zhao is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liang, Y., Zhao, S., Cheng, B., Yin, Y., Yang, H. (2022). Tackling Solitary Entities for Few-Shot Knowledge Graph Completion. 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_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-10983-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10982-9
Online ISBN: 978-3-031-10983-6
eBook Packages: Computer ScienceComputer Science (R0)