The Logical-Linguistic Model of Fact Extraction from English Texts

  • Nina Feliksivna Khairova
  • Svetlana Petrasova
  • Ajit Pratap Singh Gautam
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 639)


In this paper we suggest the logical-linguistic model that allows extracting required facts from English sentences. We consider the fact in the form of a triplet: Subject > Predicate > Object with the Predicate representing relations and the Object and Subject pointing out two entities. The logical-linguistic model is based on the use of the grammatical and semantic features of words in English sentences. Basic mathematical characteristic of our model is logical-algebraic equations of the finite predicates algebra. The model was successfully implemented in the system that extracts and identifies some facts from Web-content of a semi-structured and non-structured English text.


Logical-linguistic model Fact extraction Finite predicates algebra Triplet Grammatical features Syntactic features 


  1. 1.
    Fader, S., Soderland, O.: Etzioni Identifying relations for open information extraction. In: Conference on Empirical Methods in Natural Language Processing. Edinburgh, Scotland, pp. 1535–1545 (2011)Google Scholar
  2. 2.
    Sint, R., Schaffert, S., Stroka, S., Ferstl, R.: Combining unstructured, fully structured and semi-structured information in semantic wikis. In: Proceedings of the 4th Semantic Wiki WorkShop (SemWiki) at the 6th European Semantic Web Conference, ESWC (2009)Google Scholar
  3. 3.
    Crestan, E., Pantel, P.: Web-scale knowledge extraction from semi-structured tables. In: WWW 2010 Proceedings of the 19th International Conference on World Wide Web, pp. 1081–1082 (2010)Google Scholar
  4. 4.
    Gatterbauer, W., Bohunsky, P., Herzog, M., Krupl, B., Pollak, B.: Towards domain-independent information extraction from web tables. In: Proceedings WWW-07, pp. 71–80. Banff, Canada (2007)Google Scholar
  5. 5.
    Wong, Y.W., Widdows, D., Lokovic, T., Nigam, K.: Scalable attribute-value extraction from semi-structured text. In: 2009 IEEE International Conference on Data Mining Workshops, pp. 302–307 (2009)Google Scholar
  6. 6.
    Phillips, W., Riloff, E.: Exploiting strong syntactic heuristics and co-training to learn semantic lexicons. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2002)Google Scholar
  7. 7.
    Jones, R., Ghani, R., Mitchell, T., Riloff, E.: Active learning with multiple view feature sets. In: ECML 2003 Workshop on Adaptive Text Extraction and Mining (2003)Google Scholar
  8. 8.
    Agichtein, E., Gravano, L.: Snowball: extracting relations from large plaintext collections. In: Proceedings of the 5th ACM International Conference on Digital Libraries, pp. 85–94. San Antonio, Texas (2000)Google Scholar
  9. 9.
    Ludovic, L., Gallinari, P.: Bayesian network model for semi-structured document classification. Inf. Proc. Manage. Int. J. Spec. Issue Bayesian Netw. Inf. Retrieval 40, 807–827 (2004)Google Scholar
  10. 10.
    Rish, I.: An empirical study of the naive bayes classifier. In: Proceedings of IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence (2001)Google Scholar
  11. 11.
    Jatana, N., Sharma, K.: Bayesian spam classification: time efficient radix encoded fragmented database approach. In: 2014 International Conference on Computing for Sustainable Global Development (INDIACom), pp. 939–942 (2014)Google Scholar
  12. 12.
    Aiwu, L., Hongying, L.: Utilizing improved bayesian algorithm to identify blog comment spam. In: IEEE Symposium on Robotics and Applications(ISRA), pp. 423–426 (2012)Google Scholar
  13. 13.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: ECML 1998 Proceedings of the 10th European Conference on Machine Learning, pp. 137–142. Springer-Verlag London, UK (1998)Google Scholar
  14. 14.
    Kleinbaum, D.G., Klein, M., Pryor, E.R.: Logistic Regression: A Self-Learning Text. Springer, New York (2002)zbMATHGoogle Scholar
  15. 15.
    Baoli, L., Shiwen, Y., Qin, L.: An improved k-nearest neighbor algorithm for text categorization. In: The 20th International Conference on Computer Processing of Oriental Languages, Shenyang, China (2003)Google Scholar
  16. 16.
    Manne, S., Kotha, S. K., Fatima, S.: Text Categorization with k-nearest neighbor approach . In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications, vol.132, pp. 413–420 (2012)Google Scholar
  17. 17.
    Entezari-Maleki, R., Rezaei, A., Minaei-Bidgoli, B.: Comparison of classification methods based on the type of attributes and sample size. J. Convergence Inf. Technol. (JCIT) 4(3), 94–102 (2009)CrossRefGoogle Scholar
  18. 18.
    Mooney, R.J., Bunescu, R.: Mining knowledge from text using information extraction. Newsl. ACM SIGKDD Explor. Newsl. Nat. Lang. Process. Text Min. 7(1), 3–10 (2005)CrossRefGoogle Scholar
  19. 19.
    Yahya, M., Whang, E.S., Gupta R., Halevy A.: ReNoun: fact extraction for nominal attributes. In: Proceedings of the Conference on Empirical Methods in Natural Language (EMNLP), pp. 325–335 (2014)Google Scholar
  20. 20.
    Luckicgev, S.: Graphical notations for rule modeling. In: Giurca, A., Gašević, D., Taveter, K. (eds.) Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, Hershey, New York., vol. 1, pp. 76–98 (2009)Google Scholar
  21. 21.
    Bondarenko, M.: Shabanov-Kushnarenko, J. 2007. The intelligence theory. Kharkiv: “SMIT”, 576. (In Russian)Google Scholar
  22. 22.
    Khairova, N., Sharonova, N., Gautam, A.P.: Logic-linguistic model of fact generation from text streams of corporate information system. Int. J. Inf. Theor. Appl. 22(2), 142–152 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nina Feliksivna Khairova
    • 1
  • Svetlana Petrasova
    • 1
  • Ajit Pratap Singh Gautam
    • 1
  1. 1.National Technical University “Kharkiv Polytechnic Institute”KharkivUkraine

Personalised recommendations