PatternFinder: Pattern discovery for truth discovery

Truth discovery methods infer truths from multiple sources. These methods usually resolve conflicts based on the information on the entity level. However, due to the existence of incompleteness and the difficulty in entity matching, the information on the individual entity is often insufficient. This motivates pattern discovery, which aims to mine useful patterns across entities from a global perspective. In this paper, we introduce pattern discovery for truth discovery and formulate it as an optimization problem. To solve such a problem, we propose an algorithm called PatternFinder that jointly and iteratively learns the variables. Additionally, we also propose an optimized grouping strategy to enhance its efficiency. Experimental results on simulated and real-world datasets demonstrate the advantage of the proposed methods, which outperform the state-of-the-art baselines in terms of both effectiveness and efficiency.