Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Truth discovery and copying detection in a dynamic world
Proceedings of the VLDB Endowment
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Heterogeneous network-based trust analysis: a survey
ACM SIGKDD Explorations Newsletter
Assessing relevance and trust of the deep web sources and results based on inter-source agreement
ACM Transactions on the Web (TWEB)
Mining collective intelligence in diverse groups
Proceedings of the 22nd international conference on World Wide Web
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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.