Data Fusion: Resolving Conflicts from Multiple Sources

Dong, Xin Luna; Berti‐Équille, Laure; Srivastava, Divesh

doi:10.1007/978-3-642-38562-9_7

Cited by 27 publications

(27 citation statements)

References 12 publications

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“…As an important aspect of this area, research on resolving conflicts from multiple sources [4,5,10,22] arise various ways to handle conflicts in data integration. A common method is to conduct voting or averaging-for categorical data, the information with the highest number of occurrences is regarded as truth; for continuous claims, the mean is taken as the true value.…”

Section: Related Workmentioning

confidence: 99%

A confidence-aware approach for truth discovery on long-tail data

et al. 2014

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In many real world applications, the same item may be described by multiple sources. As a consequence, conflicts among these sources are inevitable, which leads to an important task: how to identify which piece of information is trustworthy, i.e., the truth discovery task. Intuitively, if the piece of information is from a reliable source, then it is more trustworthy, and the source that provides trustworthy information is more reliable. Based on this principle, truth discovery approaches have been proposed to infer source reliability degrees and the most trustworthy information (i.e., the truth) simultaneously. However, existing approaches overlook the ubiquitous long-tail phenomenon in the tasks, i.e., most sources only provide a few claims and only a few sources make plenty of claims, which causes the source reliability estimation for small sources to be unreasonable. To tackle this challenge, we propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. The proposed method not only estimates source reliability, but also considers the confidence interval of the estimation, so that it can effectively reflect real source reliability for sources with various levels of participation. Experiments on four real world tasks as well as simulated multi-source long-tail datasets demonstrate that the proposed method outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.

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Section: Related Workmentioning

confidence: 99%

A confidence-aware approach for truth discovery on long-tail data

et al. 2014

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“…Resolving conflicts from multiple sources have been studied in the database community for years [4,5,10,13] resulting in multiple ways to handle conflicts in data integration. Among them, one commonly used approach to eliminate conflicts for categorical data is to conduct majority voting so that information with the highest number of occurrences is regarded as the correct answer; and for continuous values, we can simply take the mean or median as the answer.…”

Section: Source Reliabilitymentioning

confidence: 99%

Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation

Gao

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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In many applications, one can obtain descriptions about the same objects or events from a variety of sources. As a result, this will inevitably lead to data or information conflicts. One important problem is to identify the true information (i.e., the truths) among conflicting sources of data. It is intuitive to trust reliable sources more when deriving the truths, but it is usually unknown which one is more reliable a priori. Moreover, each source possesses a variety of properties with different data types. An accurate estimation of source reliability has to be made by modeling multiple properties in a unified model. Existing conflict resolution work either does not conduct source reliability estimation, or models multiple properties separately. In this paper, we propose to resolve conflicts among multiple sources of heterogeneous data types. We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the truths and the multi-source observations where each source is weighted by its reliability. Different loss functions can be incorporated into this framework to recognize the characteristics of various data types, and efficient computation approaches are developed. Experiments on real-world weather, stock and flight data as well as simulated multi-source data demonstrate the necessity of jointly modeling different data types in the proposed framework 1 .

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“…Given a set of data items claimed by multiple sources, the truth finding (a.k.a. truth discovery) problem is to determine the true values of each claimed item, with various usages in information corroboration [12], and data fusion [8]. Similar to our crowdsourcing setting, existing work on truth finding also models the mutual reinforcing relationship between sources and data items, e.g., by a Bayesian model [52], maximum likelihood estimation [47], and latent credibility analysis [33].…”

Section: Related Workmentioning

confidence: 99%

Minimizing Efforts in Validating Crowd Answers

Nguyen

Thang

Weidlich

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the expert's work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20% of the questions.

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Data Fusion: Resolving Conflicts from Multiple Sources

Cited by 27 publications

References 12 publications

A confidence-aware approach for truth discovery on long-tail data

A confidence-aware approach for truth discovery on long-tail data

Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation

Minimizing Efforts in Validating Crowd Answers

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