A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia
Article No.: 27, Pages 1 - 17
Abstract
Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.
References
[1]
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data. AcM, 1247–1250.
[2]
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer et al. 2015. DBpedia–A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6, 2 (2015), 167–195.
[3]
Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web. ACM, 697–706.
[4]
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (2013), 1798–1828.
[5]
Y. Bengio. 2009. Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127. (2009).
[6]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems. MIT Press, 2787–2795.
[7]
Zhiyuan Liu, Maosong Sun, Yankai lin, and Ruobing Xie. 2016. Research on knowledge representation learning (in Chinese). J. Comput. Res. Dev. 53, 2 (2016), 247–261.
[8]
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceeding sof the 28th AAAI Conference on Artificial Intelligence.
[9]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceeding sof the 29th AAAI Conference on Artificial Intelligence.
[10]
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. 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. 687–696.
[11]
Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao. 2016. Knowledge graph completion with adaptive sparse transfer matrix. In Proceeding sof the 30th AAAI Conference on Artificial Intelligence.
[12]
Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu. 2015. TransA: An adaptive approach for knowledge graph embedding. Retrieved from https://arXiv:1509.05490.
[13]
Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu. 2015. TransG: A generative mixture model for knowledge graph embedding. Retrieved from https://arXiv:1509.05488.
[14]
He Shizhu, Liu Kang, Ji Guoliang, Zhao Jun et al. 2015. Learning to represent knowledge graphs with Gaussian embedding. In Proceedings of the 24th ACM Conference on Information and Knowledge Management. ACM, 623--632.
[15]
Xiaobin Zhao, Lirong Qiu, and Tiejun Zhao. 2011. Multi-nation language ontology knowledge base construction technology (in Chinese). J. Chinese Info. Process. 25, 4 (2011), 71–75.
[16]
Zhen Zhu and Yuan Sun. 2015. Tibetan character attribute extraction based on SVM and generalization template collaboration (in Chinese). J. Chinese Info. Process. 29, 6 (2015), 220–227.
[17]
Tianci Xia and Yuan Sun. 2018. Research on Tibetan entity relationship extraction method based on joint model (in Chinese). J. Chinese Info. Process. 32, 12 (2018), 76–83.
[18]
Lili Guo and Yuan Sun. 2016. Tibetan person attributes extraction based on BP neural network. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, 132–142.
[19]
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2012. Joint learning of words and meaning representations for open-text semantic parsing. In Artific. Intell. Stat. 127–135.
[20]
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014. A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94, 2 (2014), 233–259.
[21]
Andras Csomai and Rada Mihalcea. 2008. Linking documents to encyclopedic knowledge. IEEE Intell. Syst. 23, 5 (2008), 34–41.
[22]
David Milne and Ian H. Witten. 2008. Learning to link with wikipedia. In Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 509–518.
[23]
Razvan Bunescu and Marius Paşca. 2006. Using encyclopedic knowledge for named entity disambiguation. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics.
[24]
Chinatsu Aone and Mila Ramos-Santacruz. 2000. REES: A large-scale relation and event extraction system. In Proceedings of the 6th Conference on Applied Natural Language Processing. Association for Computational Linguistics, 76–83.
[25]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. MIT Press, 1097–1105.
[26]
Yuan Sun, Like Wang, Chaofan Chen, Tianci Xia, and Xiaobing Zhao. 2019. Improved distant supervised model in tibetan relation extraction using ELMo and attention. IEEE Access 7 (2019), 173054–173062.
[27]
ChunYang Liu, WenBo Sun, WenHan Chao, and Wanxiang Che. 2013. Convolution neural network for relation extraction. In Proceedings of the International Conference on Advanced Data Mining and Applications. Springer, 231–242.
[28]
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, Jun Zhao et al. 2014. Relation classification via convolutional deep neural network. In Proceedings of International Conference on Computational Linguistics.
[29]
Thien Huu Nguyen and Ralph Grishman. 2015. Relation extraction: Perspective from convolutional neural networks. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing. 39–48.
[30]
Yann LeCun et al. 1989. Generalization and network design strategies. In Connectionism in Perspective. Vol. 19. Citeseer.
[31]
Cicero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2015. Classifying relations by ranking with convolutional neural networks. Retrieved from https://arXiv:1504.06580.
[32]
Linlin Wang, Zhu Cao, Gerard De Melo, and Zhiyuan Liu. 2016. Relation classification via multi-level attention CNNs. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 1298–1307.
[33]
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.
[34]
Mengfei Shi. 2018. Research and Implementation of Question Answering System Based on Chinese Knowledge Base (in Chinese). Master’s thesis. East China Normal University.
[35]
Huaping Zhong, Jianwen Zhang, Zhen Wang, Hai Wan, and Zheng Chen. 2015. Aligning knowledge and text embeddings by entity descriptions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 267–272.
[36]
Dongxu Zhang, Bin Yuan, Dong Wang, and Rong Liu. 2015. Joint semantic relevance learning with text data and graph knowledge. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality. 32–40.
[37]
Tong Xiao, Jingbo Zhu, Hao Zhang, and Qiang Li. 2012. NiuTrans: An open source toolkit for phrase-based and syntax-based machine translation. In Proceedings of the ACL System Demonstrations. 19–24.
[38]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. Retrieved from https://arXiv:1301.3781.
[39]
C. J. Long, H. D. Liu, M. H. Nuo, and J. Wu. 2015. Tibetan POS tagging based on syllable tagging. J. Chin. Info. Process. 29, 5 (2015), 211–216.
[40]
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016. Representation learning of knowledge graphs with entity descriptions. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.
[41]
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. Retrieved from https://arXiv:1412.6575.
[42]
Dat Quoc Nguyen. 2017. An overview of embedding models of entities and relationships for knowledge base completion. Retrieved from https://arXiv:1703.08098.
Index Terms
- A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia
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March 2021
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Publication History
Published: 30 March 2021
Accepted: 01 January 2021
Revised: 01 January 2021
Received: 01 January 2020
Published in TALLIP Volume 20, Issue 2
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- National Natural Science Foundation of China
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