Trust analysis with clustering

ABSTRACT

Web provides rich information about a variety of objects. Trustability is a major concern on the web. Truth establishment is an important task so as to provide the right information to the user from the most trustworthy source. Trustworthiness of information provider and the confidence of the facts it provides are inter-dependent on each other and hence can be expressed iteratively in terms of each other. However, a single information provider may not be the most trustworthy for all kinds of information. Every information provider has its own area of competence where it can perform better than others. We derive a model that can evaluate trustability on objects and information providers based on clusters (groups). We propose a method which groups the set of objects for which similar set of providers provide "good" facts, and provides better accuracy in addition to high quality object clusters.

References

1. X. L. Dong, L. Berti-Equille, and D. Srivastava. Truth discovery and copying detection in a dynamic world. PVLDB, 2(1):562--573, 2009.
2. J. Pasternack and D. Roth. Knowing what to believe (when you already know something). Coling 2010, 877--885, Beijing, China.
3. Y. Sun, J. Han, P. Zhao, Z. Yin, H. Cheng, and T. Wu. Rankclus: integrating clustering with ranking for heterogeneous information network analysis. EDBT, 565--576, 2009.
4. X. Yin, J. Han, and P. S. Yu. Truth discovery with multiple conflicting information providers on the web. TKDE, 20(6):796--808, 2008.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction

  • Information systems applications

    • Data mining