The document discusses a novel method for detecting duplicates in hierarchical data, specifically in XML structures, named xmldup. This method employs a Bayesian network to analyze both content and structure for improved duplicate detection and incorporates a pruning strategy for enhanced efficiency. Experimental results illustrate that xmldup significantly outperforms traditional solutions in terms of both accuracy and execution speed.

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Efficient and Effective Duplicate Detection in Hierarchical Data
Abstract
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 datasets. XMLDup is also
able to outperform another state of the art duplicate detection solution, both in
terms of efficiency and of effectiveness. Finally, we also study how important the
structure of elements is in the duplicate detection process. We observe that, not
only structure can clearly influence the outcome, but also that, by ensuring a
structure that is adequate to the characteristics of the data, we can actually improve
the quality of the results.
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