skip to main content
10.1145/2401603.2401613acmotherconferencesArticle/Chapter ViewAbstractPublication PagesracsConference Proceedingsconference-collections
research-article

Predicting website correctness from consensus analysis

Authors Info & Claims
Published:23 October 2012Publication History

ABSTRACT

Websites vary in terms of reliability. One could assume that NASA's website will be very accurate for Astronomy questions. Wikipedia is less accurate but is still more accurate than a generic Google search. In this research we ask a large number of "factoid" questions to several different search engines. We collect those responses and determine the correctness of each candidate answer. The answers are grouped by website source, and are compared to other websites to infer website correctness.

References

  1. X. Yin, W. Tan and C. Liu, "FACTO: A Fact Lookup Engine Based on Web Tables," in World Wide Web Conference (WWW), Hyderabad, India, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Brin and L. Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Computer Networks and ISDN Systems 30, pp. 107--117, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. X. Yin, J. Han and P. S. Yu, "Truth Discovery with Multiple Conflicting Information Providers on the Web," Knowledge Discovery and Data Mining (KDD), 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. X. L. Dong, L. Berti-Equille and D. Srivastava, "Integrating Conflicting Data: The Role of Source Dependence," Very Large Databases (VLDB), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Galland, A. Marian, S. Abiteboul and P. Senellart, "Corroborating Information from Disagreeing Views," Web Search and Data Mining (WSDM), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. O'Hara and T. Bylander, "Numeric Query Answering on the Web," International Journal on Semantic Web and Information Systems, pp. 1--17, January-March 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. B. Katz, S. Felshin, D. Yuret, A. Ibrahim, J. Lin, G. Marton, A. J. McFarland and B. Temelkuran, "Omnibase: Uniform Access to Heterogeneous Data for Question Answering," in Proceedings of the 7th International Workshop on Applications of Natural Language to Information Systems, Stockholm, Sweden, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. V. I. Levenshtein, "Binary Codes Capable of Correcting Deletions, Insertions and Reversals," Cybernetics and Control Theory, pp. 845--848, 1965.Google ScholarGoogle Scholar
  9. X. Li and D. Roth, "Learning Question Classifiers: The Role of Semantic Information," in International Conference on Computational Linguistics, Taipei, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Ko, L. Si and E. Nyberg, "A Probabilistic Graphical Model for Joint Answer Ranking in Question Answering," in Proceedings of SIGIR, Amsterdam, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. Kwok, O. Etzioni and D. S. Weld, "Scaling question answering to the web," ACM Transactions on Information Systems, vol. 19, no. 3, pp. 242--262, July 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Barcala, J. Vilares, M. A. Alonso, J. Grana and m. Vilares, "Tokenization and Proper Noun Recognition for Information Retrieval," Departamento de Computacion, Universidade da Coruna, La Coruna, Spain.Google ScholarGoogle Scholar
  13. D. Roussinov, W. Fan and J. Robles-Flores, "Beyond keywords: Automated question answering on the web," Communications of the ACM, vol. 51, no. 9, pp. 60--65, September 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Predicting website correctness from consensus analysis

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        RACS '12: Proceedings of the 2012 ACM Research in Applied Computation Symposium
        October 2012
        488 pages
        ISBN:9781450314923
        DOI:10.1145/2401603

        Copyright © 2012 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 23 October 2012

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate393of1,581submissions,25%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader