Abstract
The link prediction on knowledge graphs is now one of the challenges that are gaining a lot of interest from the academic community. The leading solution for this problem is based on graph embedding. Recently, in embedding approaches, convolutional neural networks (CNN) have produced promising results, especially the HypER model. The HypER model outperforms the preceding approaches to maximize the quantity of information from the source entities and relations. However, HypER and other CNN-based methods only focus on retaining information (i.e., structure) of knowledge graphs in low dimension embedding spaces while ignoring literals of the entities. However, the literals can also have a significant impact on relation construction. As a result, this paper proposes an improved model called HypERLit, which is based on the HypER model and incorporates literals. Experiments prove that the role of literals significantly influences the accuracy of the prediction model on the benchmark datasets, including FB15k, FB15k-237, and YAGO3-10. Furthermore, our model outperforms the HypER and other CNN-based models on almost standard metrics.
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
Balažević, I., Allen, C., Hospedales, T.M.: Hypernetwork Knowledge Graph Embeddings. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11731, pp. 553–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30493-5_52
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Ha, D., Dai, A., Le, Q.V.: Hypernetworks. arXiv preprint arXiv:1609.09106 (2016)
Jiang, X., Wang, Q., Wang, B.: Adaptive convolution for multi-relational learning. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 978–987 (2019)
Kristiadi, A., Khan, M.A., Lukovnikov, D., Lehmann, J., Fischer, A.: Incorporating literals into knowledge graph embeddings. In: Ghidini, C., Hartig, O., Maleshkova, M., Svátek, V., Cruz, I., Hogan, A., Song, J., Lefrançois, M., Gandon, F. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 347–363. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_20
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)
Mahdisoltani, F., Biega, J., Suchanek, F.: Yago3: a knowledge base from multilingual Wikipedias. In: 7th Biennial Conference on Innovative Data Systems Research. CIDR Conference (2014)
Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on convolutional neural network. arXiv preprint arXiv:1712.02121 (2017)
Sarker, M.K., et al.: Wikipedia knowledge graph for explainable ai. In: Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC) (11/2020 2020)
Singhal, A.: Introducing the knowledge graph: Things, not strings, May 2012. https://blog.google/products/search/introducing-knowledge-graph-things-not
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)
Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., Talukdar, P.: Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3009–3016 (2020)
Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3), 485 (2021)
Acknowledgements
This research is funded by the University of Science, VNU-HCM, Vietnam under grant number CNTT 2022-02 and Advanced Program in Computer Science.
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
Le, T., Tran, T., Le, B. (2022). Embedding and Integrating Literals to the HypER Model for Link Prediction on Knowledge Graphs. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-21743-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21742-5
Online ISBN: 978-3-031-21743-2
eBook Packages: Computer ScienceComputer Science (R0)