Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Human Powered Data Cleaning for Probabilistic Reachability Queries on Uncertain Graphs

Language

English

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.

Publication Date

3-17-2017

Source Publication Title

IEEE Transactions on Knowledge and Data Engineering

Volume

29

Issue

7

Start Page

1452

End Page

1465

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

DOI

10.1109/TKDE.2017.2684166

Link to Publisher's Edition

https://ieeexplore.ieee.org/document/7880660/

ISSN (print)

1041-4347

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