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A Discovery Method of Anteroposterior Correlation for Big Data Era

  • Takafumi NakanishiEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 569)

Abstract

In this paper, we present a new knowledge extraction method on Big data era.We introduce new concepts, anteroposterior correlation, and propose an extraction method of anteroposterior correlation. The anteroposterior correlation means the correlation based on the time anteroposterior relation. We consider that Heterogeneity, continuity, and visualization are the most critical features of Big data analytics, which provides a scale and connection merits based on them. No current data analysis methods are based on opened assumptions. Big data analytics provides a new data analysis method based on opening assumptions. In this paper, we especially focus on an aspect of heterogeneity. We discover a correlation in consideration of the continuity of time. By our method, we effectively discover relationships between heterogeneous things, events and phenomena. The anteroposterior correlations are represented in relative comparison with each conditional probability distribution. The one of the features of our method is a measurement correlation by using conditional probability. That is, we calculate the correlation relative by representing all in conditional probability, no absolutely. Our method is determined higher correlation by comparison to each heterogeneous thing, event and phenomenon. This is the most important points on the Big data era. When you apply current association rule extraction techniques, you obtain too big rule base to organize them. By our method, we realize the one of the methods for decision mining.

Keywords

Information Extraction Common Cold Vector Space Model Great East Japan Earthquake Nuisance Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Magazine Communications of the ACM CACM Homepage Archive 18(11), 613–620 (1975)zbMATHGoogle Scholar
  2. 2.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Kitagawa, T., Kiyoki, Y.: A mathematical model of meaning and its application to multidatabase systems. In: RIDE-IMS 1993: Proceedings of the 3rd International Workshop on Research Issues in Data Engineering: Interoperability in Multidatabase Systems, pp. 130–135 (1993)Google Scholar
  4. 4.
    Kiyoki, Y., Kitagawa, T., Hayama, T.: A metadatabase system for semantic image search by a mathematical model of meaning. SIGMOD Rec. 23(4), 34–41 (1994)CrossRefGoogle Scholar
  5. 5.
    Takano, K., Kiyoki, Y.: A superordinate and subordinate relationship computation method and its application to aerospace engineering information. In: ACST 2007: Proceedings of the Third Conference on IASTED International Conference, Anaheim, CA, USA, pp. 510–516 (2007)Google Scholar
  6. 6.
    Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: An on-line lexical database. Journal of Lexicography 3(4), 235–244 (1990)CrossRefGoogle Scholar
  7. 7.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  8. 8.
    Kim, Y., Kim, J.: A model of knowledge based information retrieval with hierarchical concept graph. Journal of Documentation 46(2), 113–136 (1990)CrossRefGoogle Scholar
  9. 9.
    Lee, J., Kim, M., Lee, Y.: Information retrieval based on conceptual distance in is-a hierarchies. Journal of Documentation 49(2), 188–207 (1993)CrossRefGoogle Scholar
  10. 10.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: IJCAI: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)Google Scholar
  11. 11.
    Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Trans. Inf. Syst. 21(1), 64–93 (2003)CrossRefGoogle Scholar
  12. 12.
    Wimalasuriya, D., Dou, D.: Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science 36(3), 306–323 (2010)CrossRefGoogle Scholar
  13. 13.
    Saggion, H., Funk, A., Maynard, D., Bontcheva, K.: Ontology-Based Information Extraction for Business Intelligence. In: Aberer, K., et al. (eds.) ASWC/ISWC 2007. LNCS, vol. 4825, pp. 843–856. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Wu, F., Hoffmann, R., Weld, D.S.: Information extraction from Wikipedia: moving down the long tail. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 731–739. ACM, New York (2008), http://doi.acm.org/10.1145/1401890.1401978, doi:10.1145/1401890.1401978CrossRefGoogle Scholar
  15. 15.
    Cimiano, P., Handschuh, S., Staab, S.: Towards the self-annotating web. In: Proceedings of the 13th International Conference on World Wide Web (WWW 2004), pp. 462–471. ACM, New York (2004), http://doi.acm.org/10.1145/988672.988735, doi:10.1145/988672.988735CrossRefGoogle Scholar
  16. 16.
    McDowell, L.K., Cafarella, M.: Ontology-Driven Information Extraction with OntoSyphon. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 428–444. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Maedche, A., Neumann, G., Staab, S.: Bootstrapping an ontology-based information extraction system. In: Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.) Intelligent Exploration of the Web, pp. 345–359. Physica-Verlag GmbH, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Maedche, A., Staab, S.: The Text-to-Onto Ontology Learning Environment. In: Software Demonstration at the 8th International Comference Conceputual Structures. Springer, Berlin (2000)Google Scholar
  19. 19.
    Buitelaar, P., Siegel, M.: The Text-to-Onto Ontology Learning Environment. In: Proceedings of the 5th International Conference on Language Resources and Evaluation, pp. 2321–2324 (2006)Google Scholar
  20. 20.
    Embley, D., David Embley, W.: Toward semantic understanding: an approach based on information extraction ontologies. In: Schewe, K.-D., Williams, H. (eds.) Proceedings of the 15th Australasian Database Conference (ADC 2004), vol. 27, pp. 3–12. Australian Computer Society, Inc., Darlinghurst (2004)Google Scholar
  21. 21.
    Li, Y., Bontcheva, K.: Hierarchical, perceptron-like learning for ontology-based information extraction. In: Proceedings of the 16th International Conference on World Wide Web (WWW 2007), pp. 777–786. ACM, New York (2007), http://doi.acm.org/10.1145/1242572.1242677, doi:10.1145/1242572.1242677CrossRefGoogle Scholar
  22. 22.
    Hwang, C.: Incompletely and imprecisely speaking: Using dynamic ontologies for representing and retrieving information. In: Proceedings of the 6th International Workshop on Ontology-Based Information Extraction System, Kaiserslautern, Germany (1999)Google Scholar
  23. 23.
    Yildiz, B., Miksch, S.: ontoX - A Method for Ontology-Driven Information Extraction. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part III. LNCS, vol. 4707, pp. 660–673. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Todirascu, A., Romary, L., Bekhouche, D.: Vulcain - An Ontology-Based Information Extraction System. In: Andersson, B., Bergholtz, M., Johannesson, P. (eds.) NLDB 2002. LNCS, vol. 2553, pp. 64–75. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  25. 25.
    Vargas-Vera, M., Motta, E., Domingu, J., Shum, S., Lanzoni, M.: Knowledge extraction by using an ontology-based annotation tool. In: Proceedings of the workshop on knowledge markup and semantic annotation, USA, ACM Press, New York (2001)Google Scholar
  26. 26.
    Popov, B., Kiryakov, A., Ognyanoff, D., Monov, D., Kirilov, A.: KIM–a semantic platform for information extraction and retrieval.? Natural Language Engineering 10(3-4), 375–392 (2004)CrossRefGoogle Scholar
  27. 27.
    Adrian, B., Hees, J., van Elst, L., Dengel, A.: iDocument: Using Ontologies for Extracting and Annotating Information from Unstructured Text. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS, vol. 5803, pp. 249–256. Springer, Heidelberg (2009)Google Scholar
  28. 28.
    Adar, E., Weld, D.S., Bershad, B.N., Gribble, S.D.: Why We Search: Visualizing and Predicting User Behavior. WWW (2007)Google Scholar
  29. 29.
    Sadilek, A., Kautz, H., Silenzio, V.: Predicting Disease Transmission from Geo-Tagged Micro-Blog Data. AAAI (2012)Google Scholar
  30. 30.
    Gilbert, E., Karahalios, K.: Widespread Worry and the Stock Market. AAAI (2010)Google Scholar
  31. 31.
    Berman, J.: Principles of Big Data Preparing. Elsevier / Morgan Kaufmann (2013)Google Scholar
  32. 32.
    Minelli, M., Chambers, M., Dhiraj, A.: Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. Wiley (2013)Google Scholar
  33. 33.
    Finley, K.: Facebook Says Its New Data Center Will Run Entirely on Wind, WIRED (2013), http://www.wired.com/wiredenterprise/2013/11/facebook-iowa-wind/
  34. 34.
    Greenberg, P.: 10 Reasons 2014 will be the Year of Small Data, ZDNet. (2013), http://www.zdnet.com/10-reasons-2014-will-be-the-year-of-small-data-7000023667/
  35. 35.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Center for Global Communications (GLOCOM)International University of JapanTokyoJapan

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