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Provenance and scientific workflows: challenges and opportunities
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Truth Discovery with Multiple Conflicting Information Providers on the Web
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On the provenance of non-answers to queries over extracted data
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ACM Computing Surveys (CSUR)
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Provenance query evaluation: what's so special about it?
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Integrating conflicting data: the role of source dependence
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Truth discovery and copying detection in a dynamic world
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Corroborating information from disagreeing views
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Knowing what to believe (when you already know something)
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A framework for corroborating answers from multiple web sources
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TRAMP: understanding the behavior of schema mappings through provenance
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Global detection of complex copying relationships between sources
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SOLOMON: seeking the truth via copying detection
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A Bayesian approach to discovering truth from conflicting sources for data integration
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Truth finding on the deep web: is the problem solved?
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Despite the abundance of useful information on the Web, different Web sources often provide conflicting data, some being out-of-date, inaccurate, or erroneous. Data fusion aims at resolving conflicts and finding the truth. Advanced fusion techniques apply iterative MAP (Maximum A Posteriori) analysis that reasons about trustworthiness of sources and copying relationships between them. Providing explanations for such decisions is important for a better understanding, but can be extremely challenging because of the complexity of the analysis during decision making. This paper proposes two types of explanations for data-fusion results: snapshot explanations take the provided data and any other decision inferred from the data as evidence and provide a high-level understanding of a fusion decision; comprehensive explanations take only the data as evidence and provide an in-depth understanding of a fusion decision. We propose techniques that can efficiently generate correct and compact explanations. Experimental results show that (1) we generate correct explanations, (2) our techniques can significantly reduce the sizes of the explanations, and (3) we can generate the explanations efficiently.