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The Big Data era features a huge amount of data that are contributed bynumerous sources and used by many critical data-driven applications. Due to thevarying reliability of sources, it is common to see conflicts among themulti-source data, making it difficult to determine which data sources totrust. Recently, truth discovery has emerged as a means of addressing thischallenging issue by determining data veracity jointly with estimating thereliability of data sources. A fundamental issue with current truth discoverymethods is that they generally assume only one true value for each object,while in reality, objects may have multiple true values. In this paper, wepropose a graph-based approach, called SmartMTD, to unravel the truth discoveryproblem beyond the single-truth assumption, or the multi-truth discoveryproblem. SmartMTD models and quantifies two types of source relations toestimate source reliability precisely and to detect malicious agreement amongsources for effective multi-truth discovery. In particular, two graphs areconstructed based on the modeled source relations. They are further used toderive the two aspects of source reliability (i.e., positive precision andnegative precision) via random walk computation. Empirical studies on two largereal-world datasets demonstrate the effectiveness of our approach.

SmartMTD: A Graph-Based Approach for Effective Multi-Truth Discovery