Abstract: Entity resolution (ER) is the fundamental problem of data integration and cleaning,while inconsistency reconciliation(IR) further improves the resolution performance through reconciling inconsistent pairs resolved by existing diverse ER approaches.However,previous IR approaches have a limitation that the reconciliation solution has no quality guarantee.To solve this problem,this paper firstly proposed a quality control agent based on probability inference,denoted as QCAgent.QCAgent does not require any manually labeled pair,and can automatically output reconciliation result with the highest recall on the premise of satisfying the given precision threshold.Its core idea is as follows.Firstly,the outlier detection model is utilized to estimate the matching probability for each inconsistent pair,and then the estimated precision and recall are regarded as the environmental feedback according to these probabilities.Next,the binary search algorithm is used to select a flipping solution as the next action of QCAgent,which can make flipped reconciliation result satisfy the precision requirement with the highest recall.Then the outlier detection model is retrained by using the new consistent pairs,and the recall and precision of flipped reconciliation result are estimated.The iterative process terminates until the newest estimated precision meets the constraints.On the real data set,the experimental results show that QCAgent can effectively solve the quality control problem of reconciliation result.

Key words: Agent, Entity resolution, Inconsistency reconciliation, Precision, Quality control