Advertisement

WikiSeeAlso: Suggesting Tangentially Related Concepts (See also links) for Wikipedia Articles

  • Sahiti Labhishetty
  • Ayesha Siddiqa
  • Rajivteja Nagipogu
  • Sutanu Chakraborti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10682)

Abstract

Wikipedia is the pervasive knowledge source for widely utilized applications like Google’s Knowledge Graph, IBM’s Watson and Apple’s Siri system. Wikipedia articles contain internal links and See also section links. According to Wikipedia, one of the purposes of See also links is to enable readers to explore tangentially related topics. Currently, Wikipedia relies on human judgments for adding See also links. We attempt to automate the process of See also recommendation by utilizing the aspects of Wikipedia articles like category knowledge, Backlink and the ESA concept vector similarity and external knowledge retrieved by web search engine. Our proposed ensemble based approach combines similarities obtained from these aspects to give a final prediction score. We evaluate our approach on datasets of Wikipedia articles and present our empirical comparison and case studies results with the state-of-the art approaches. We envisage that this work will aid Wikipedia editors and readers to facilitate information search.

References

  1. 1.
    Adafre, S.F., de Rijke, M.: Discovering missing links in wikipedia. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, Chicago, Illinois, USA, 21–25 August 2005, pp. 90–97 (2005)Google Scholar
  2. 2.
    West, R., Precup, D., Pineau, J.: Completing wikipedia’s hyperlink structure through dimensionality reduction. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 2–6 November 2009, pp. 1097–1106 (2009)Google Scholar
  3. 3.
    Noraset, T., Bhagavatula, C., Downey, D.: Adding high-precision links to wikipedia. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 651–656 (2014)Google Scholar
  4. 4.
    Siddiqa, A., Tendulkar, A.V., Chakraborti, S.: Wikiaug: augmenting wikipedia by suggesting credible hyperlinks. CICLing: J. Res. Comput. Sci. (2017)Google Scholar
  5. 5.
    Schwarzer, M., Schubotz, M., Meuschke, N., Breitinger, C., Markl, V., Gipp, B.: Evaluating link-based recommendations for wikipedia. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, 19–23 June 2016, pp. 191–200 (2016)Google Scholar
  6. 6.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6–12 January 2007, pp. 1606–1611 (2007)Google Scholar
  7. 7.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sahiti Labhishetty
    • 1
  • Ayesha Siddiqa
    • 1
  • Rajivteja Nagipogu
    • 1
  • Sutanu Chakraborti
    • 1
  1. 1.Indian Institute of Technology MadrasChennaiIndia

Personalised recommendations