Fuzzy duplicate detection aims at identifying multiple representations of real-world objects stored in a data source, and is a task of critical practical relevance in data cleaning, data mining, or data integration. It has a long history for relational data stored in a single table (or in multiple tables with equal schema). Algorithms for fuzzy duplicate detection in more complex structures, e.g., hierarchies of a data warehouse, XML data, or graph data have only recently emerged. These algorithms use similarity measures that consider the duplicate status of their direct neighbors, e.g., children in hierarchical data, to improve duplicate detection effectiveness. In this paper, we propose a novel method for fuzzy duplicate detection in hierarchical and semi-structured XML data. Unlike previous approaches, it not only considers the duplicate status of children, but rather the probability of descendants being duplicates. Probabilities are computed efficiently using a Bayesian network. Experiments show the proposed algorithm is able to maintain high precision and recall values, even when dealing with data containing a high amount of errors and missing information. Our proposal is also able to outperform a state-of-the-art duplicate detection system on three different XML databases.

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Abstract. Fuzzy duplicate detection aims at identifying multiple representations of real-world objects in a data source, and is a task of critical relevance in data cleaning, data mining, and data integration tasks. It has a long history for relational data, stored in a single table or in multiple tables with an equal schema. However, algorithms for fuzzy duplicate detection in more complex structures, such as hierarchies of a data warehouse, XML data, or graph data have only recently emerged. These algorithms use similarity measures that consider the duplicate status of their direct neighbors to improve duplicate detection effectiveness. In this chapter, we study different approaches that have been proposed for XML fuzzy duplicate detection. Our study includes a description and analysis of the different approaches, as well as a comparative experimental evaluation performed on both artificial and real-world data. The two main dimensions used for comparison are the methods effectiveness and efficiency. Our comparison shows that the DogmatiX system [44] is the most effective overall, as it yields the highest recall and precision values for various kinds of differences between duplicates. Another system, called XMLDup [27] has a similar performance, being most effective especially at low recall values. Finally, the SXNM system [36] is the most efficient, as it avoids executing too many pairwise comparisons, but its effectiveness is greatly affected by errors in the data.

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Duplicate detection consists in finding objects that, although having different representations in a database, correspond to the same real world entity. This is typically achieved by comparing all objects to each other, which can be unfeasible for large datasets. Blocking strategies have been devised to reduce the number of objects to compare, at the cost of loosing some duplicates. However, these strategies typically rely on user knowledge to discover a set of parameters that optimize the comparisons, while minimizing the loss. Also, they do not usually optimize the comparison between each pair of objects. In this paper, we propose a blocking method of combining two optimization strategies: one to select which objects to compare and another to optimize pair-wise object comparisons. In addition, we propose a machine learning approach to determine the required parameters, without the need of user intervention. Experiments performed on several datasets show that not only we are able to effectively determine the optimization parameters, but also to significantly improve efficiency, while maintaining an acceptable loss of recall.

Duplicate detection consists in finding objects that, although having different representations in a database, correspond to the same real world entity. This is typically achieved by comparing all objects to each other, which can be unfeasible for large datasets. Strategies have been devised to reduce the number of objects to compare, at the cost of loosing some duplicates. However, these strategies typically rely on user knowledge to discover a set of parameters that optimize the comparisons, while minimizing the loss. Also, they do not usually optimize the comparison between each pair of objects. In this paper, we propose a method of combining two optimization strategies: one to select which objects to compare and another to optimize pair-wise object comparisons. In addition, we propose a machine learning approach to determine the required parameters, without the need of user intervention. Experiments performed on several datasets show that not only we are able to effectively determine the optimization parameters, but also to significantly improve efficiency, while maintaining an acceptable loss of recall.