AllegatorTrack: Combining and reporting results of truth discovery from multi-source data

Waguih, Dalia Attia; Goel, Narendra S.; Hammady, Hossam M.; Berti‐Équille, Laure

doi:10.1109/icde.2015.7113396

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…This is known under various names such as truth discovery [6,[16][17][18], fact finding, data fusion [3,7,12,14], etc. ; see [7,13] for early surveys and [1,15] for recent comparative evaluations. The simplest truth finding approach is majority voting which trusts the answers provided by the largest number of equally reliable sources.…”

Section: Related Workmentioning

confidence: 99%

Truth Finding with Attribute Partitioning

Horincar

Senellart

et al. 2015

Proceedings of the 18th International Workshop on Web and Databases

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Truth finding is the problem of determining which of the statements made by contradictory sources is correct, in the absence of prior information on the trustworthiness of the sources. A number of approaches to truth finding have been proposed, from simple majority voting to elaborate iterative algorithms that estimate the quality of sources by corroborating their statements. In this paper, we consider the case where there is an inherent structure in the statements made by sources about real-world objects, that imply different quality levels of a given source on different groups of attributes of an object. We do not assume this structuring given, but instead find it automatically, by exploring and weighting the partitions of the sets of attributes of an object, and applying a reference truth finding algorithm on each subset of the optimal partition. Our experimental results on synthetic and real-world datasets show that we obtain better precision at truth finding than baselines in cases where data has an inherent structure.

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Section: Related Workmentioning

confidence: 99%

Truth Finding with Attribute Partitioning

Horincar

Senellart

et al. 2015

Proceedings of the 18th International Workshop on Web and Databases

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“…Table 2 shows the top ten websites that provide the most records, with their quality values obtained by one of our methods MBM (see Section 5.2 for details). It should be noted that most datasets used in previous works for categorical truth discovery [12,20] are not suitable for our multi-truth-finding problem. The three real datasets used in our work are comparable to those datasets in size.…”

Section: The Datasetsmentioning

confidence: 99%

“…Many of them develop probabilistic graphical models for handling categorical values [24], numerical values [23], ordinal values [9] and knowledge base triples [7]. Waguih et al [20] summarize and experimentally evaluate these truth-finding methods.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Bayesian Approach for Effective Multi-Truth Discovery

Wang

Sheng

Fang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truthfinding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truthfinding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truthfinding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.

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“…If true, the process diverges into two branches: i) adding the value to the existing group (lines [11][12][13][14] and ii) setting up a new group for the value (lines [15][16][17][18]. In the first case, Algorithm 2 invokes itself, taking the updated solution (with the new value added to the last group) and the updated sublist (with the first value removed) as inputs (line 12).…”

Section: Content-based Groupingmentioning

confidence: 99%

“…Waguih et al [18] experimentally evaluated the performance of several truth discovery algorithms on three computing nodes on both real-world and synthetic datasets with various configurations, and concluded that most algorithms have efficiency problems. For example, both the algorithms based on Maximum Likelihood Estimation (MLE) and those on probabilistic graphical approaches were too computationally expensive to be applied to large scale problems.…”

Section: Introductionmentioning

confidence: 99%

Approximate Truth Discovery via Problem Scale Reduction

Wang

Sheng

Fang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.

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AllegatorTrack: Combining and reporting results of truth discovery from multi-source data

Cited by 14 publications

References 10 publications

Truth Finding with Attribute Partitioning

Truth Finding with Attribute Partitioning

An Integrated Bayesian Approach for Effective Multi-Truth Discovery

Approximate Truth Discovery via Problem Scale Reduction

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