Reconciling inconsistent data in probabilistic XML data integration

The problem of dealing with inconsistent data while integrating XML data from different sources is an important task, necessary to improve data integration quality. Typically, in order to remove inconsistencies, i.e. conflicts between data, data cleaning (or repairing) procedures are applied. In this paper, we present a probabilistic XML data integration setting. A probability is assigned to each data source and its probability models the reliability level of the data source. In this way, an answer (a tuple of values of XML trees) has a probability assigned to it. The problem is how to compute such probability, especially when the same answer is produced by many sources. We consider three semantics for computing such probabilistic answers: by-peer, by-sequence, and by-subtree semantics. The probabilistic answers can be used for resolving a class of inconsistencies violating XML functional dependencies defined over the target schema. Having a probability distribution over a set of conflicting answers, we can choose the one for which the probability of being correct is the highest.