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Issue No. 07 - July (2017 vol. 29)
ISSN: 1041-4347
pp: 1452-1465
Xin Lin , Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
Yun Peng , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Byron Choi , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Jianliang Xu , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
ABSTRACT
Uncertain graph models are widely used in real-world applications such as knowledge graphs and social networks. To capture the uncertainty, each edge in an uncertain graph is associated with an existential probability that signifies the likelihood of the existence of the edge. One notable issue of querying uncertain graphs is that the results are sometimes uninformative because of the edge uncertainty. In this paper, we consider probabilistic reachability queries, which are one of the fundamental classes of graph queries. To make the results more informative, we adopt a crowdsourcing-based approach to clean the uncertain edges. However, considering the time and monetary cost of crowdsourcing, it is a problem to efficiently select a limited set of edges for cleaning that maximizes the quality improvement. We prove that the edge selection problem is #P-hard. In light of the hardness of the problem, we propose a series of edge selection algorithms, followed by a number of optimization techniques and pruning heuristics for reducing the computation time. Our experimental results demonstrate that our proposed techniques outperform a random selection by up to 27 times in terms of the result quality improvement and the brute-force solution by up to 60 times in terms of the elapsed time.
INDEX TERMS
Cleaning, Probabilistic logic, Uncertainty, Databases, Crowdsourcing, Data models, Knowledge engineering
CITATION

X. Lin, Y. Peng, B. Choi and J. Xu, "Human-Powered Data Cleaning for Probabilistic Reachability Queries on Uncertain Graphs," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 7, pp. 1452-1465, 2017.
doi:10.1109/TKDE.2017.2684166
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