att_abstract={{Many applications rely on Web data and extraction systems to accomplish
knowledge-driven tasks. Web information is not curated,
so many sources provide inaccurate, or conflicting information.
Moreover, extraction systems introduce additional noise to the data.
We wish to automatically distinguish correct data and erroneous
data for creating a cleaner set of integrated data. Previous work
has shown that a naïve voting strategy that trusts data provided by
the majority or at least a certain number of sources may not work
well in the presence of copying between the sources. However,
correlation between sources can be much broader than copying:
sources may provide data from complementary domains (negative
correlation), extractors may focus on different types of information
(negative correlation), and extractors may apply common rules in
extraction (positive correlation, without copying). In this paper we
present novel techniques modeling correlations between sources
and applying it in truth finding. We provide a comprehensive
evaluation of our approach on three real-world datasets with different
characteristics, as well as on synthetic data, showing that our
algorithms outperform the existing state-of-the-art techniques.}},
	att_categories={C_BB.1, C_NSS.2, C_IIS.5, C_IIS.6, C_IIS.1},
	att_copyright_notice={{(c) ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2014 {{, 2014-06-22}}.
	author={Divesh Srivastava and Ravali Pochampally and Anish Das Sarma and Xin Luna Dong and Alexandra Meliou},
	institution={{ACM SIGMOD 2014}},
	title={{Fusing Data with Correlations}},