Scalable Iterative Graph Duplicate Detection
Melanie Herschel • Felix Naumann • Sascha Szott • Maik Taubert
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011.
Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. In this paper, we present a novel method for XML duplicate detection, called XMLDup. XMLDup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation, a novel pruning strategy, capable of significant gains over the unoptimized version of the algorithm, is presented. Through experiments, we show that our algorithm is able to achieve high precision and recall scores in several data sets. XMLDup is also able to outperform another state-of-the-art duplicate detection solution, both in terms of efficiency and of effectiveness.