Abstract
Knowledge representation learning aims to transform entities and relationships in a knowledge base into computable forms, so that an efficient calculation can be realized. It is of great significance to the construction, reasoning and application of knowledge base. The traditional translation-based models mainly obtain the finite dimension vector representation of entities or relationships by projecting to finite dimensional Euclidean space. These simple and effective methods greatly improve the efficiency and accuracy of knowledge representation. However, they ignore a fact that the semantic space develops and grows forever with the passage of time. Finite dimensional Euclidean space is not enough in capacity for vectorizing infinitely growing semantic space. Time is moving forward forever, so knowledge base would expand infinitely with time. This determines that the vector representation of entities and relationships should support infinite capacity. We fill the gap by putting forward TransI (Translating Infinite Dimensional Embeddings) model, which extends knowledge representation learning from finite dimensions to infinite dimensions. It is trained by Trend Smooth Distance based on the idea of continuous infinite dimension vector representation. The Training Efficiency of TransI model is obviously better than TransE under the same setting, and its effect of Dimension Reduction Clustering is more obvious.
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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. ACM (2008)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Fabian, M., Gjergji, K., Gerhard, W., et al.: Yago: a core of semantic knowledge unifying wordnet and wikipedia. In: 16th International World Wide Web Conference, WWW, pp. 697–706 (2007)
Fan, M., Zhou, Q., Chang, E., Zheng, T.F.: Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing (2014)
Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 84–94 (2015)
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. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 687–696 (2015)
Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Ma, S., Ding, J., Jia, W., Wang, K., Guo, M.: TransT: type-based multiple embedding representations for knowledge graph completion. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 717–733. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_43
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Miller, G.: WordNet: a lexical database for English. Commun. ACM 38, 39–41 (1995)
Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2316–2325 (2016)
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Guo, X., Gao, N., Wang, L., Wang, X. (2019). TransI: Translating Infinite Dimensional Embeddings Based on Trend Smooth Distance. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_46
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DOI: https://doi.org/10.1007/978-3-030-29551-6_46
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