Abstract
Knowledge graph completion aims to predict missing relations between known entities. In this paper, we consider the method of knowledge graph embedding for hyper-relational data, which is common in knowledge graphs. Previous models such as Trans(E, H, R) and CTransR either are insufficient to embed hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be plausible for generalization and might not ideally reflect the real knowledge. To overcome the issues, we propose a novel model named TransHR, which transforms the vectors of hyper-relations between a pair of entities into an individual vector acting as a translation between them. We experimentally evaluate our model on two typical tasks including link prediction and triple classification. The results demonstrate that TransHR significantly outperforms Trans(E, H, R) and CTransR especially for hyper-relational data.
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References
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)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: Joint learning of words and meaning representations for open-text semantic parsing. In: International Conference on Artificial Intelligence and Statistics, pp. 127–135 (2012)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 26, pp. 2787–2795. Curran Associates Inc., Red Hook (2013). http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence, No. EPFL-CONF-192344 (2011)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)
Chen, D., Socher, R., Manning, C.D., Ng, A.Y.: Learning new facts from knowledge bases with neural tensor networks and semantic word vectors (2013). arXiv preprint arxiv:1301.3618
Fabian, M., Gjergji, K., Gerhard, W.: Yago: a core of semantic knowledge unifying wordnet and wikipedia. In: 16th International World Wide Web Conference, WWW, pp. 697–706 (2007)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 623–632. ACM (2015)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix (2016)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: AAAI, vol. 3, p. 5 (2006)
Lin, Y., Liu, Z., Sun, M.: Modeling relation paths for representation learning of knowledge bases (2015). arXiv preprint arxiv:1506.00379
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI (2015)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995). http://doi.acm.org/10.1145/219717.219748
Miller, K., Jordan, M.I., Griffiths, T.L.: Nonparametric latent feature models for link prediction. In: Advances in Neural Information Processing Systems, pp. 1276–1284 (2009)
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 809–816 (2011)
Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing yago: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 271–280. ACM (2012)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658. ACM (2008)
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)
Sutskever, I., Tenenbaum, J.B., Salakhutdinov, R.R.: Modelling relational data using Bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems, pp. 1821–1828 (2009)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119. Citeseer (2014)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI - Association for the Advancement of Artificial Intelligence, July 2014. http://research.microsoft.com/apps/pubs/default.aspx?id=225161
Zhu, J.: Max-margin nonparametric latent feature models for link prediction (2012). arXiv preprint arxiv:1206.4659
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Zhou, M., Zhang, C., Han, X., Ji, Y., Hu, Z., Qiu, X. (2016). Knowledge Graph Completion for Hyper-relational Data. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_20
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