Abstract
Knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation based versions are known to provide state-of-the-art results. In recent works, developments on factorisation based knowledge graph embedding models were mostly limited to enhancing the ComplEx and the DistMult models, as they can efficiently provide predictions within linear time and space complexity. In this work, we aim to extend the works of the ComplEx and the DistMult models by proposing a new factorisation model, TriModel, which uses three part embeddings to model a combination of symmetric and asymmetric interactions between embeddings. We perform an empirical evaluation for the TriModel model compared to other tensor factorisation models on different training configurations (loss functions and regularisation terms), and we show that the TriModel model provides the state-of-the-art results in all configurations. In our experiments, we use standard benchmarking datasets (WN18, WN18RR, FB15k, FB15k-237, YAGO10) along with a new NELL based benchmarking dataset (NELL239) that we have developed.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
All the benchmarking datasets can be downloaded using the following url: https://figshare.com/s/88ea0f4b8b139a13224f.
- 2.
We have used the code provided at: https://github.com/facebookresearch/kbc for the evaluation of the models: CP-N3, CP-N3-R, ComplEx-N3 and ComplEx-N3-R.
References
Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD Conference, pp. 1247–1250. ACM (2008)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)
Bouchard, G., Singh, S., Trouillon, T.: On approximate reasoning capabilities of low-rank vector spaces. In: AAAI Spring Symposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches. AAAI Press (2015)
Chen, W., Liu, T., Lan, Y., Ma, Z., Li, H.: Ranking measures and loss functions in learning to rank. In: NIPS, pp. 315–323. Curran Associates, Inc. (2009)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: AAAI. AAAI Press (2018)
Gardner, M., Mitchell, T.M.: Efficient and expressive knowledge base completion using subgraph feature extraction. In: EMNLP, pp. 1488–1498. The Association for Computational Linguistics (2015)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS. JMLR Proceedings, vol. 9, pp. 249–256. JMLR.org (2010)
Kadlec, R., Bajgar, O., Kleindienst, J.: Knowledge base completion: baselines strike back. In: Rep4NLP@ACL, pp. 69–74. Association for Computational Linguistics (2017)
Lacroix, T., Usunier, N., Obozinski, G.: Canonical tensor decomposition for knowledge base completion. In: ICML. JMLR Workshop and Conference Proceedings, vol. 80, pp. 2869–2878. JMLR.org (2018)
Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: CIDR (2015). www.cidrdb.org
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Mitchell, T.M., et al.: Never-ending learning. Commun. ACM 61(5), 103–115 (2018)
Mnih, A., Kavukcuoglu, K.: Learning word embeddings efficiently with noise-contrastive estimation. In: NIPS, pp. 2265–2273 (2013)
Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.Q.: A novel embedding model for knowledge base completion based on convolutional neural network. In: NAACL-HLT (2), pp. 327–333. Association for Computational Linguistics (2018)
Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961. AAAI Press (2016)
Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809–816. Omnipress (2011)
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
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: EMNLP, pp. 1499–1509. The Association for Computational Linguistics (2015)
Trouillon, T., Nickel, M.: Complex and holographic embeddings of knowledge graphs: a comparison. In: StarAI, vol. abs/1707.01475 (2017)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2071–2080. JMLR.org (2016)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Learning multi-relational semantics using neural-embedding models. In: ICLR (2015)
Acknowledgements
This work has been supported by the TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway, Ireland (supported by the Science Foundation Ireland grant 12/RC/2289). The GPU card used in our experiments is granted to us by the Nvidia GPU Grant Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohamed, S.K., Nováček, V. (2019). Link Prediction Using Multi Part Embeddings. In: Hitzler, P., et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-21348-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21347-3
Online ISBN: 978-3-030-21348-0
eBook Packages: Computer ScienceComputer Science (R0)