Truth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multisource data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence information in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.
Recently, Wang et al. REF design three models (i.e., the byproduct model, the joint model and the synthesis model) for enhancing existing truth discovery methods.
Empowering Truth Discovery with Multi-Truth Prediction
CIKM '16
Computer Science