Read e-book online An Introduction to Duplicate Detection PDF

By Felix Naumann, Melanie Herschel, M. Tamer Ozsu

ISBN-10: 1608452204

ISBN-13: 9781608452200

With the ever expanding quantity of knowledge, facts caliber difficulties abound. a number of, but assorted representations of an identical real-world gadgets in facts, duplicates, are probably the most interesting info caliber difficulties. the results of such duplicates are unsafe; for example, financial institution clients can receive reproduction identities, stock degrees are monitored incorrectly, catalogs are mailed a number of instances to a similar loved ones, and so forth. immediately detecting duplicates is hard: First, reproduction representations aren't exact yet a bit fluctuate of their values. moment, in precept all pairs of files can be in comparison, that is infeasible for big volumes of knowledge. This lecture examines heavily the 2 major elements to beat those problems: (i) Similarity measures are used to instantly determine duplicates while evaluating documents. Well-chosen similarity measures increase the effectiveness of reproduction detection. (ii) Algorithms are constructed to accomplish on very huge volumes of knowledge in look for duplicates. Well-designed algorithms increase the potency of replica detection. ultimately, we talk about tips on how to overview the good fortune of replica detection. desk of Contents: information detoxing: advent and Motivation / challenge Definition / Similarity features / replica Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography

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Extra info for An Introduction to Duplicate Detection

Example text

Whereas previously discussed measures focus on string similarity, phonetic similarity focuses on the sounds of spoken words, which may be very similar despite large spelling differences. For instance, the two strings Czech and cheque are not very similar; however, they are barely distinguishable phonetically. Therefore, they have a large phonetic similarity. Soundex is a very common phonetic coding scheme, and the idea behind the computation of phonetic similarity is to first transform strings (or tokens) into their phonetic representation and to then apply the similarity measures on strings or tokens on the soundex representation [Bourne and Ford, 1961].

As a simple example, assume the tokenization function splits a string into tokens based on whitespace characters. Then, the string Sean Connery results in the set of tokens {Sean, Connery}. As we will show throughout our discussion, the main advantage of token-based similarity measures is that the similarity is less sensitive to word swaps compared to similarity measures that consider a string as a whole (notably edit-based measures). That is, the comparison of Sean Connery and Connery Sean will yield a maximum similarity score because both strings contain the exact same tokens.

We may decide that the similarity score in this case is lower than, for instance, the similarity of two books being equal except for one author in the sequence of authors both candidates have. Such a fine distinction is not necessary for data complying to a relational schema. As a consequence, when processing XML data, we need to be aware of this distinction and decide how to handle the subtle differences it may cause, notably during similarity computation. The third property, the element cardinality adds complexity to similarity measurement when we consider duplicate detection in relational data with relationships between candidate types.

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An Introduction to Duplicate Detection by Felix Naumann, Melanie Herschel, M. Tamer Ozsu


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