In the Big Data era, truth discovery has served as a promising technique to solve conflicts in the facts provided by numerous data sources. The most significant challenge for this task is to estimate source reliability and select the answers supported by high quality sources. However, existing works assume that one data source has the same reliability on any kinds of entity, ignoring the possibility that a source may vary in reliability on different domains. To capture the influence of various levels of expertise in different domains, we integrate domain expertise knowledge to achieve a more precise estimation of source reliability. We propose to infer the domain expertise of a data source based on its data richness in different domains. We also study the mutual influence between domains, which will affect the inference of domain expertise. Through leveraging the unique features of the multi-truth problem that sources may provide partially correct values of a data item, we assign more reasonable confidence scores to value sets. We propose an integrated Bayesian approach to incorporate the domain expertise of data sources and confidence scores of value sets, aiming to find multiple possible truths without any supervision. Experimental results on two real-world datasets demonstrate the feasibility, efficiency and effectiveness of our approach.
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