Exploring Relevance as Truth Criterion on the Web and Classifying Claims in Belief Levels

Document Type : Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)

Authors

Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.

Abstract

The Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this paper, we have attempted to answer the question whether the truth is relevant. We conducted an experimental study in which we analyzed and compared the results of two different truth discovery methods: Relevance-based sources ranking and Majority vote. We have found that the truth is not always held by the most relevant sources on the web. Sometimes the truth is given by the majority vote of the crowd. In addition, we have proposed a method of presenting the results of truth discovery with gradual degrees of belief. A method that allows to configure and target the desired level of trust.

Keywords


Al-Araji, Z. J., Ahmad, S. S. S., Al-Lamy, H. A., Al-Salihi, M. W., Al-Shami, S. A., Mohammed, H., & Al-Taweel, M. H. (2019). Truth Discovery Using the TrustChecker Algorithm on Online Quran Tafseer. In Intelligent and Interactive Computing (pp. 71-80). Springer, Singapore.
Dong, X. L., Berti-Equille, L., & Srivastava, D. (2009). Integrating conflicting data: the role of source dependence. Proceedings of the VLDB Endowment, 2(1), 550-561.
Dong, X. L., Saha, B., & Srivastava, D. (2012). Less is more: Selecting sources wisely for integration. Proceedings of the VLDB Endowment, 6(2), 37-48.
Gurjar, K., & Moon, Y. S. (2016). Comparative Study of Evaluating the Trustworthiness of Data Based on Data Provenance. Journal of Information Processing Systems, 12(2).
Jung, W., Kim, Y., & Shim, K. (2019). Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion. arXiv preprint arXiv:1904.10217.
Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., & Han, J. (2014, June). 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 (pp. 1187-1198).
Li, X., Dong, X. L., Lyons, K., Meng, W., & Srivastava, D. (2015). Truth finding on the deep web: Is the problem solved? arXiv preprint arXiv:1503.00303.
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., ... & Han, J. (2016). A survey on truth discovery. ACM Sigkdd Explorations Newsletter, 17(2), 1-16.
Pasternack, J., & Roth, D. (2010, August). Knowing what to believe (when you already know something). In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 877-885). Association for Computational Linguistics.
Roa-Valverde, A. J., & Sicilia, M. A. (2014). A survey of approaches for ranking on the web of data. Information Retrieval, 17(4), 295-325.
Yin, X., & Tan, W. (2011, March). Semi-supervised truth discovery. In Proceedings of the 20th international conference on World wide web (pp. 217-226).
Yin, X., Han, J., & Philip, S. Y. (2008). Truth discovery with multiple conflicting information providers on the web. IEEE Transactions on Knowledge and Data Engineering, 20(6), 796-808.
Zendaoui, F., & Hidouci, W. K. (2019a)
Volume 12, Issue 2
Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)
2020
Pages 1-12