Abstract
Knowledge graph, a semantic network, to organize and store data is increasingly interested in the research community and businesses such as Google, Facebook, Amazon. For the machine learning models to work well in this data, we need to prepare good quality negative samples. Generating these negative examples is challenging in the knowledge graph because it is pretty hard to determine whether a link that does not appear in the graph is a negative or positive sample. In this paper, we apply the generative adversarial network to the ConvKB method to generate negative samples, thereby producing a better graph embedding. Experiments show that our approach has quality improvement compared to the original method on well-known datasets.
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
References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS), pp. 1–9 (2013)
Cai, L. and Wang, W.Y: KBGAN: adversarial learning for knowledge graph embeddings. arXiv preprint arXiv:1711.04071 (2017)
Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Cheng, K., Zhu, Y., Zhang, M. and Sun, Y.: NoiGAN: noise aware knowledge graph embedding with adversarial learning (2019)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE. VLDB J. 24(6), 707–730 (2015)
Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Guo, L., Sun, Z., Hu, W.: Learning to exploit long-term relational dependencies in knowledge graphs. In: International Conference on Machine Learning, pp. 2505–2514. PMLR (2019)
Han, X., Zhang, C., Ji, Y., Hu, Z.: A dilated recurrent neural network-based model for graph embedding. IEEE Access 7, 32085–32092 (2019)
Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning: Methods, Systems, Challenges, pp. 219. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-05318-5
Kotnis, B., Nastase, V.: Analysis of the impact of negative sampling on link prediction in knowledge graphs. arXiv preprint arXiv:1708.06816 (2017)
Meilicke, C., Fink, M., Wang, Y., Ruffinelli, D., Gemulla, R., Stuckenschmidt, H.: Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 3–20. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_1
Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: IJCAI, pp. 3137–3143 (2019)
Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)
Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of NAACL-HLT (2017)
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), pp. 11–33 (2015)
Ortona, S., Meduri, V.V., Papotti, P.: Robust discovery of positive and negative rules in knowledge bases. In: IEEE 34th International Conference on Data Engineering (ICDE), pp. 1168–1179 (2018)
Peng, B., Min, R., Ning, X.: CNN-based dual-chain models for knowledge graph learning. arXiv preprint arXiv:1911.06910 (2019)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017) (2017)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É, Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080, PMLR (2016)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vu, T., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 2180–2189 (2019)
Wang, X., Xu, Y., He, X., Cao, Y., Wang, M. and Chua, T.S.: Reinforced negative sampling over knowledge graph for recommendation. In: Proceedings of The Web Conference 2020, pp. 99–109 (2020)
Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing Top-N collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 785–788 (2013)
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 57–66 (2015)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (2018, page to appear)
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Workshop on CVSMC, pp. 57–66 (2015)
Wang, Y., Ruffinelli, D., Broscheit, S., Gemulla, R.: On evaluating embedding models for knowledge base completion. Technical report. arXiv:1812.06410 (2018)
Acknowledgements
This research is funded by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam, Grant number CNTT 2021-03 and Advanced Program in Computer Science.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Le, T., Pham, T., Le, B. (2021). Negative Sampling for Knowledge Graph Completion Based on Generative Adversarial Network. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-88081-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88080-4
Online ISBN: 978-3-030-88081-1
eBook Packages: Computer ScienceComputer Science (R0)