Feel-Phy: An Intelligent Web-Based Physics QA System

  • Kwong Seng FongEmail author
  • Chih How Bong
  • Zahrah Binti Ahmad
  • Norisma Binti Idris
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)


Feel-Phy is a computerized and unmanned question answering system which is able to solve open-ended Physics problems, providing adaptive guidance and retrieve relevant resources to user inputs. Latent Semantic Indexing (LSI) is employed to process the user inputs and retrieve relevant references. The proposed architecture for Feel-Phy constitutes of four basic modules: data extraction, question classification, solution identification and answer formulation. The data extraction module is used to construct a Physics knowledge base. The question classification module is used to identify question type and understand the question. The solution identification module computes the answer to the question and also retrieve the top n most relevant resource references to the users. Finally, the last module, answer formulation is to present the results to the users. Our preliminary experiments have shown that this proposed method is able to solve well-structure Physics question and retrieve relevant references to the users.


LSI QA system Calculation Information retrieval Physics problem Open text question 



This research is supported by research grant RACE/b(6)/1098/2013(06), KPM. Besides, I would like to express my deepest gratitude to my supervisor, Dr. Bong Chih How for his extraordinary efforts in providing guidance and motivation. Besides, I also want to thank Associate Prof. Dr. Zahrah Binti Ahmad and Dr. Norisma Binti Idris for suggesting a lot of ideas and comments about the system in order to improve my system. I also wish to take this opportunity to acknowledge my heartiest thanks to all my friends who involved directly or indirectly towards the accomplishment of my system.


  1. 1.
    Adamic, L.A., Zhang, J., Bakshy, E., Ackerman, M.S.: Knowledge sharing and yahoo answers: everyone knows something. In: Proceedings of the 17th International Conference on World Wide Web, pp. 665–674. ACM (2008)Google Scholar
  2. 2.
    Bradford, R.: Why lsi? Latent semantic indexing and information retrieval (2009)Google Scholar
  3. 3.
    Cui, L., Wang, C.: An intelligent q&a system based on the lda topic model for the teaching of database principles. World Trans. Eng. Technol. Educ. 12(1), 26–30 (2014)Google Scholar
  4. 4.
    Das, S., Catterjee, R., Mandal, J.K.: An approach for creating framework for automated question generation from instructional objective. In: Satapathy, S.C., et al. (eds.) Proceedings of the Second International Conference on Computer and Communication Technologies. AISC, vol. 379, pp. 527–535. Springer, India (2016)CrossRefGoogle Scholar
  5. 5.
    Deshmukh, A., Hegde, G., Lathi, R., Govikarn, S.: A literature survey on latent semantic indexing. In: International Conference on Computing, p. 100 (2012)Google Scholar
  6. 6.
    Green Jr., B.F., Wolf, A.K., Chomsky, C., Laughery, K.: Baseball: an automatic question-answerer. Papers Presented at the 9–11 May 1961, Western Joint IREAIEE-ACM Computer Conference, pp. 219–224. ACM (1961)Google Scholar
  7. 7.
    Hirschman, L., Gaizauskas, R.: Natural language question answering: the view from here. Nat. Lang. Eng. 7(4), 275–300 (2001)CrossRefGoogle Scholar
  8. 8.
    Kamdi, R.P., Agrawal, A.J.: Keywords based closed domain question answering system for indian penal code sections and indian amendment laws. Int. J. Intell. Syst. Appl. (IJISA) 7(12), 57 (2015)Google Scholar
  9. 9.
    Lobo, R.: Score a programme product features, December 2009.
  10. 10.
    Martin, D.I., Berry, M.W.: Mathematical foundation behind latent semantic analysis. In: Handbook of Latent Semantic Analysis, pp. 35–56 (2007)Google Scholar
  11. 11.
    Polya, G.: How to Solve It: A New Aspect of Mathematical Method. Princeton University Press, Princeton (2014)zbMATHGoogle Scholar
  12. 12.
    Tsai, C., Yih, W.T., Burges, C.: Web-based question answering: Revisiting askmsr. Technical report MSR-TR-2015-20, Microsoft Research (2015)Google Scholar
  13. 13. How it works (2016).
  14. 14.
    Woods, W.A.: Progress in natural language understanding: an application to lunar geology. In: Proceeding of the 4–8 June 1973, National Computer Conference and Exposition, pp. 441–450. ACM (1973)Google Scholar
  15. 15.
    Woods, W.A.: Semantics and quantification in natural language question answering. Adv. Comput. 17(3), 1–87 (1978)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Kwong Seng Fong
    • 1
    Email author
  • Chih How Bong
    • 1
  • Zahrah Binti Ahmad
    • 2
  • Norisma Binti Idris
    • 3
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.Centre for Foundation Studies in ScienceUniversity of MalayaKuala LumpurMalaysia
  3. 3.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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