Integrating Conflicting Data: The Role of Source Dependence

By Xin Luna Dong, Laure Berti-Équille, Divesh Srivastava. Proceedings of the VLDB Endowment, 2009

468 citations recorded by the source corpus.

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.

Subfields: Information Systems, Information Systems and Management, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/70fd8392d104ef35d85b07fbfab1990f3e4145a2

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Citations in the corpus

Estimate Dynamic Road Travel Time Based on Uncertainty Feedback (2014, background): “In the last decade, El Faouzi [2], Bachmann [3], Dong [4, 5], and Huang [6] have reported on data fusion techniques related to traffic services.”
A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014, methodology): “Many truth discovery methods [8,16,19,20,23,23–26,31, 34–37] use weighted voting or averaging more or less to achieve the truths, which overcome the issue of conventional voting or averaging schema that assumes all the sources are equally reliable.”
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, background): “…collaborators from a medical school—who are currently engaged in extracting information from scientific articles to populate a structured data repository—we find that using data fusion can be challenging for users despite the numerous approaches proposed in the literature [9, 14, 21, 22, 28, 38].”
Knowing What to Believe (when you already know something) (2010, background): “AccuVote (Dong et al., 2009a; Dong et al., 2009b) improves on this by using calculated source dependence (where one source derives its information from another) to give higher credibility to independent sources.”
Big data integration (2013, background): “To address such veracity related challenges, techniques have been proposed to find the single truth from conflicting values [49], [32], [48], [39], [38], [18], [9], [47] and to find multiple truths [50].”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, methodology): “, 2008; Dong et al., 2009, 2015; Li et al., 2011, 2014, 2015; Pasternack and Roth, 2011, 2013; Ma et al., 2015; Zhi et al., 2015; Gao et al., 2015; Lyu et al., 2017) have focused on structured data with the goal of addressing the problem of conflict resolution amongst multisource data. Nakashole and”
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration (2012, methodology): “While this dataset has been used previously [4, 14], there all the authors observed by each source for the same book were concatenated as one single claim.”
A Survey on Truth Discovery (2015, methodology): “Similar to [13], both the copying relationship and true values are inferred in an iterative procedure.”
CDAS: A Crowdsourcing Data Analytics System (2012, methodology): “We adopt and extend the approach proposed in the data fusion [4] for integrating con ﬂ icting results in the CDAS.”
Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation (2014, methodology): “Note that in [9], other algorithms have been proposed to tackle source dependency issues in resolving conflicts, which are not compared here because we do not consider source dependency in this paper but leave it for future work.”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “In the future, we plan to deploy more sophisticated copy detection mechanisms, such as those in [9].”
Fusing data with correlations (2014, methodology): “For our datasets, [6] applies only to BOOK dataset by considering the author list as a whole, but not the other datasets.”
Truth Finding on the Deep Web: Is the Problem Solved? (2012, methodology): “However, the copy-detection method proposed in [6] falls short in the presence of numerical values as it ignores value similarity and granularity.”