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A multi-level text representation model within background knowledge based on human cognitive process for big data analysis

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Abstract

Text representation is part of the most fundamental work in text comprehension, processing, and search. Various kinds of work has been proposed to mine the semantics in texts and then to represent them. However, most of them only focus on how to mine semantics from the text itself, while few of them take the background knowledge into consideration, which is very important to text understanding. In this paper, on the basis of human cognitive process, we propose a multi-level text representation model within background knowledge, called TRMBK. It is composed of three levels, which are machine surface code, machine text base and machine situational model. All of them are able to be constructed automatically to acquire semantics both inside and outside of the texts. Simultaneously, we also propose a method to establish background knowledge automatically and offer supports for the current text comprehension. Finally, experiments and comparisons have been presented to show the better performance of TRMBK.

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References

  1. Li, Q., Wang, J., Chen, Y.P., Lin, Z.: User comments for news recommendation in forum-based social media. Inf. Sci. 180(24), 4929–4939 (2010)

    Article  Google Scholar 

  2. Li, Y., Wang, Y., Huang, X.: A relation-based search engine in semantic web. IEEE Trans. Knowl. Data Eng. 19(2), 273–282 (2007)

    Article  MathSciNet  Google Scholar 

  3. Gu, X.J., Li, Q., Diao, R.J.: Research of E-learning intelligent affective model based on BDI agent with learning materials. Adv. Comput. Sci. Intell. Syst. Environ. 104, 99–104 (2011)

    Article  Google Scholar 

  4. Wu, C.J., Chung, J.M., Lu, C.Y., Lee, H.M., Ho, J.M.: Using web-mining for academic measurement and scholar recommendation in expert finding system. IEEE/WIC/ International Conference on Web Intelligence and Intelligent Agent Technology, pp. 288–291 (2011)

  5. Lécué, F., Mehandjiev, N.: Seeking quality of web service composition in a semantic dimension. IEEE Trans. Knowl. Data Eng. 23(6), 942–959 (2011)

    Article  Google Scholar 

  6. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  7. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  8. Luo, X., Wei, X., Zhang, J.: Guided game-based learning using fuzzy cognitive maps. IEEE Trans. Learn. Technol. 3(4), 344–357 (2010)

    Article  Google Scholar 

  9. Luo, X., Cai, C., Hu, Q.: Text knowledge representation model based on human concept learning. IEEE International Conference on Cognitive Informatics, pp. 383–390 (2010)

  10. Hofmann, T.: Probabilistic latent semantic indexing. International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 56–73 (1999)

  11. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  12. Mccallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Intell. Res. 30(1), 249–272 (2007)

    Google Scholar 

  13. Fikes, R., McGuinness, D.L.: An axiomatic semantics for RDF, RDF schema, and DAML-ONT. Technical Report Knowledge Systems Laboratory, Stanford University, Stanford, CA (2000)

  14. http://www.w3.org/TR/owl-features/

  15. http://www.cs.umd.edu/projects/plus/SHOE/

  16. Waugh, N.C., Norman, D.A.: Primary memory. Psychol. Rev. 72(2), 89–104 (1965)

    Article  Google Scholar 

  17. Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036–1060 (2004)

    Article  Google Scholar 

  18. Snaider, J., Mccall, R., Franklin, S.: The LIDA framework as a general tool for AGI. International Conference on Artificial General Intelligence, pp. 793–807 (2011)

  19. Atkinson, R.C., Shiffrin, R.M.: Human memory: a proposed system and its control processes. Psychol. Learn. Motiv. 2, 89–195 (1968)

    Article  Google Scholar 

  20. Baddeley, A.: Working memory: looking back and looking forward. Nat. Rev. Neurosci. 4(10), 829–839 (2003)

    Article  Google Scholar 

  21. Kintsch, W., Van Dijk, T.A.: Toward a model of text comprehension and production. Psychol. Rev. 85(5), 363 (1978)

    Article  Google Scholar 

  22. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)

  23. Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)

    MATH  Google Scholar 

  24. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63(2), 81–97 (1956)

    Article  Google Scholar 

  25. Wei, X., Luo, X., Li, Q., Zhang, J.: ExNa: an efficient search pattern for search engines. International Conference on Web-Age Information Management, pp. 699–702 (2014)

  26. Wei, X., Zeng, D.D.: ExNa: an efficient search pattern for semantic search engines. Concurr. Comput.: Pract. Exp. doi:10.1002/cpe.3818 (2016)

  27. Luo, X., Xu, Z., Yu, J., Chen, X.: Building association link network for semantic link on web resources. IEEE Trans. Autom. Sci. Eng. 8(3), 482–494 (2011)

    Article  Google Scholar 

  28. Xu, Z., Wei, X., Luo, X., Liu, Y., Mei, L., Hu, C., et al.: Knowle: a semantic link network based system for organizing large scale online news events. Futur. Gener. Comput. Syst. 43–44, 40–50 (2015)

    Article  Google Scholar 

  29. Xu, Z., Luo, X., Mei, L., Hu, C.: Measuring the semantic discrimination capability of association relations. Concurr. Comput. Pract. Exp. 26(2), 380–395 (2014)

    Article  Google Scholar 

  30. Xu, Z., Liu, Y., Mei, L., Hu, C., Chen, L.: Semantic based representing and organizing surveillance big data using video structural description technology. J. Syst. Softw. 102, 217–225 (2015)

    Article  Google Scholar 

  31. Xu, Z., et al.: Mining temporal explicit and implicit semantic relations between entities using web search engines. Futur. Gener. Comput. Syst. 37, 468–477 (2014)

    Article  Google Scholar 

  32. Kim, S.H., Chung, K.: Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust. Comput. 18(2), 747–759 (2015)

    Article  Google Scholar 

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Acknowledgments

Research work reported in this paper was partly supported by the Science Foundation of Shanghai under Grant No. 16ZR1435500 and by the National Science Foundation of China under Grant No. 61562020.

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Correspondence to Xiao Wei.

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This paper is an extended version of the paper presented on the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing.

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Wei, X., Zhang, J., Zeng, D.D. et al. A multi-level text representation model within background knowledge based on human cognitive process for big data analysis. Cluster Comput 19, 1475–1487 (2016). https://doi.org/10.1007/s10586-016-0616-3

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  • DOI: https://doi.org/10.1007/s10586-016-0616-3

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