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Construction and Verification of Knowledge Base of Political & Economy News based on Mixed Algorithm of Subgraph Feature Extraction and RESCAL

Volume 13, Number 8, December 2017, pp. 1268-1280
DOI: 10.23940/ijpe.17.08.p9.12681280

Pin Wu, Juanjuan Luo, Yonghua Zhu, Wenjie Zhang

Department of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

(Submitted on October 29, 2017; Revised on November 12, 2017; Accepted on December 3, 2017)



Abstract:

With the intelligent development of digital government management services and the advancement of Knowledge Graph study, it is necessary and possible to construct and verify a sound knowledge base of political and economic news to satisfy the users’ requirement of learning the information. Due to the high profession and diversity of political and economic news data, the entity link in the initially constructed knowledge base is lacking completeness. Meanwhile, the high frequency of data update leads to the iterative update of knowledge base. To address the problems, this paper builds a comparatively effective system in which we apply the reasoning results to the construction and iterative update of the political and economic news knowledge base. Then, a syncretic reasoning algorithm based on Subgraph Feature Extraction (SFE) and the factorization of a three-way tensor (RESCAL) is proposed to predict a link and accomplish the reasoning. Using the field data of political and economic news as a case of engineering application, the system we built effectively solves the incompleteness of the entity link in the initial knowledge base and the iterative update problem. The function of knowledge reasoning module and iteration module of knowledge base construction and autonomous updating system are verified by designing and implementing knowledge reasoning, as well as updating knowledge iteration. The experimental results demonstrate the effectiveness and feasibility of the functions of the knowledge base construction and autonomous updating system are verified.

 

References: 30

      1. B. Antoine, U. Nicolas, G. D. Alberto, W. Jason, and Y. Oksana, "Translating Embeddings for Modeling Multi-relational Data," in proceedings of Neural Information Processing Systems (NIPS) , pp. 1-9, South Lake Tahoe, United States, Dec 2013
      2. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, "DBpedia: a nucleus for a web of open data," The semantic web, vol. 4825, pp. 722–735, 2007
      3. W. Y. Bing, Z. D. Hong, L. X. Wen, Z. Dong, and L. K. Biao "Knowledge Graph Reasoning Based on Paths of Tensor Factorization," Pattern Recognition and Artificial Intelligence, vol. 30 (5), pp. 473-480, 2017
      4. X. Bo, X. Yong, L. Jiaqing, X. C. Hao, L. Bin, C. W. Yun, and X. Y. Hua, "CN-DBpedia: A Never Ending Chinese Knowledge Extraction System," in proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 428-438, Montreal, Canada, June 2017
      5. K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, "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, Vancouver, Canada, June 2008
      6. A. Bordes, J. Weston, R. Collobert, and Y. Bengio, "Learning structured embeddings of knowledge bases," in proceedings of the 25th Annual Conference on Artificial Intelligence (AAAI), Buenos Aires, Argentina, July 2011
      7. K. W. Chang, S. W. T. Yih, B. Yang, and C. Meek, “Typed tensor decomposition of knowledge bases for relation extraction,” in Proceedings of Conference on Empirical Methods in Natural Language, pp. 171-180, Irina, Matveeva, April 2014
      8. W. Cui, Y. Xiao, and W. Wang, "KBQA: an online template based question answering system over freebase," in proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 4240–4241, New York City, US, July 2016
      9. A. Garcia-Duran, A. Bordes, and N. Usunier, "Composing relationships with translations," Diss. CNRS, Heudiasyc, 2015
      10. M. Gardner and M. M. Tom, "Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction," EMNLP, pp. 1488-1498, Lisbon, Portugal, September 2015
      11. K. Guu, M. John, and L. Percy, "Traversing knowledge graphs in vector space," arXiv preprint arXiv:1506.01094, 2015
      12. HanLP, https://github.com/hankcs/HanLP
      13. G. T. Kolda and W. B. Brett, "Tensor factorizations and applications," SIAM, pp. 455-500, 2009
      14. D. Krompaß, M. Nickel, and V. Tresp, "Large-scale factorization of type-constrained multi-relational data," Data Science and Advanced Analytics (DSAA), pp. 18-24, 2014.
      15. N. Lao, M. Tom, and W. C. William, "Random walk reasoning and learning in a large scale knowledge base," in proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 529-539, Edinburgh, Scotland, UK, July 2011
      16. Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu, "Modeling relation paths for representation learning of knowledge bases," arXiv preprint arXiv:1506.00379, 2015
      17. A. Neelakantan, R. Benjamin, and M.C. Andrew, "Compositional vector space models for knowledge base reasoning," in proceedings of 2015 aaai spring symposium series, pp. 156–166, Austin, Texas, USA, January 2015
      18. M. Nickel and V. Tresp, "Logistic tensor factorization for multi-relational data," arXiv preprint arXiv:1306.2084, 2013
      19. M. Nickel, V. Tresp, and H. P. Kriegel, "A three-way model for collective learning on multi-relational data," in proceedings of the 28th international conference on machine learning (ICML-11), pp. 809-816, Bellevue, Washington, USA, June 2011
      20. M. Nickel, V. Tresp, and H. P. Kriegel, "Factorizing yago: scalable machine learning for linked data," in proceedings of the 21st international conference on World Wide Web, pp. 271-280, Lyon, France, April 2012
      21. M. Nickel, J. X. Yan, and V. Tresp, "Reducing the rank in relational factorization models by including observable patterns," Advances in Neural Information Processing Systems, pp. 1179-1187, Palais des Congrès de Montréal, Dec 2014
      22. X. Niu, X. Sun, H. Wang, S. Rong, G. Qi, and Y. Yu, "Zhishi. meweaving chinese linking open data," The Semantic Web–ISWC 2011, pp.205-220, Bonn, Germany, October 2011
      23. W. Quan, B. Wang, and L. Guo, "Knowledge Base Completion Using Embeddings and Rules," in proceedings of International Joint Conference on Artificial Intelligence, pp. 1859-1866, Buenos Aires, Argentina, July 2015
      24. M. Sabou , K. Bontcheva, and A. Scharl, "Crowdsourcing research opportunities: lessons from natural language processing," in proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, pp. 17, Graz, Austria, September 2012
      25. Singhal, and Amit, "Introducing the Knowledge Graph: Things, Not Strings," Official Blog (of Google), 2012
      26. R. Socher, D. Chen, C. D. Manning, and A. Ng, "Reasoning with neural tensor networks for knowledge base completion," Advances in neural information processing systems, pp. 926-934, 2013
      27. F. M. Suchanek, G. Kasneci, and G. Weikum, "Yago: a core of semantic knowledge," In proceedings of the 16th International Conference on World Wide Web, pp. 697–706, Banff, Alberta, Canada, May 2007
      28. C. L. Wei, F. Y. Song, and Z. D. Yan, "Extracting relations from the Web via weakly supervised learning," Journal of Computer Research and Development, pp. 1825-1835, 2013
      29. D. Yang, J. He, H. Qin, Y. Xiao, and W. Wang, "A graph-based recommendation across heterogeneous domains," in proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 463-472, Melbourne, Australia, October 2015
      30. B. Yang, W.T. Yih, X. He, J. Gao, and L. Deng, "Embedding entities and relations for learning and inference in knowledge bases," arXiv preprint arXiv:1412.6575, 2014

           

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