Resolving conflicts among unbalanced multi-source data when multi-value objects exist

When considering multi-value objects, the inevitable unbalanced data distribution is overlooked by the existing truth discovery methods. In this work, we propose a confidence interval based approach (CIMTD) to tackle this issue. We estimate source reliability from two aspects, i.e., the ability to claim the correct number of value(s) and specific value(s). To reflect real reliability for both 'big' and 'small' sources, confidence intervals of enriched estimation are considered. While estimating source reliability, uncertainty degrees are introduced to model object differences. Confidence intervals are also considered to reflect real uncertainty degrees for both 'hot' and 'cold' objects. CIMTD outperforms baseline methods on real-world datasets.