SLFTD: A Subjective Logic Based Framework for Truth Discovery

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1064)


Finding truth from various conflicting candidate values provided by different data sources is called truth discovery, which is of vital importance in data integration. Several algorithms have been proposed in this area, which usually have similar procedure: iteratively inferring the truth and provider’s reliability on providing truth until converge. Therefore, an accurate provider’s reliability evaluation is essential. However, no work pays attention to “how reliable this provider continuously providing truth”. Therefore, we introduce subjective logic, which can record both (1) the provider’s reliability of generating truth, and (2) reliability of provider continuously doing so. Our proposed methods provides a better evaluation for data providers, and based on which, truth are discovered more accurately. Our framework can handle both categorical and numerical data, and can identify truth in either a generative or discriminative way. Experiments on two popular real world datasets, Book and Population, validates that our proposed subjective logic based framework can discover truth much more accurately than state-of-art methods.


Data fusion Truth discovery Subjective logic 


  1. 1.
    Dong, X.L., Berti-Equille, L., Srivastava, D.: Integrating conflicting data: the role of source dependence. In: Proceedings of the VLDB Endowment 2.1, pp. 550–561 (2009)CrossRefGoogle Scholar
  2. 2.
    Dong, X.L., Saha, B., Srivastava, D.: Less is more: selecting sources wisely for integration. In: Proceedings of the VLDB Endowment, vol. 6. no. 2. VLDB Endowment (2012)CrossRefGoogle Scholar
  3. 3.
    Galland, A., et al.: Corroborating information from disagreeing views. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining. ACM (2010)Google Scholar
  4. 4.
    Yin, X., Tan, W.: Semi-supervised truth discovery. In: Proceedings of the 20th International Conference on World Wide Web. ACM (2011)Google Scholar
  5. 5.
    Yin, X., Han, J., Philip, S.Y.: Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng. 20(6), 796–808 (2008)CrossRefGoogle Scholar
  6. 6.
    Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), p. 2. IEEE (2013) Google Scholar
  7. 7.
    Pelechrinis, K., et al.: Automatic evaluation of information provider reliability and expertise. World Wide Web 18(1), 33–72 (2015)CrossRefGoogle Scholar
  8. 8.
    Pasternack, J., Roth, D.: Knowing what to believe (when you already know something). In: Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics (2010)Google Scholar
  9. 9.
    Li, Q., et al.: Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. ACM (2014)Google Scholar
  10. 10.
    Li, Q., et al.: A confidence-aware approach for truth discovery on long-tail data. Proc. VLDB Endow. 8(4), 425–436 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhao, B., Han, J.: A probabilistic model for estimating real-valued truth from conflicting sources. In: Proceedings of QDB (2012)Google Scholar
  12. 12.
    Jøsang, A.: Subjective Logic. Springer, Heidelberg (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computing and InformationUniversity of PittsburghPittsburghUSA
  2. 2.Department of Information and Communication TechnologyUniversity of AgderKristiansandNorway

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