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

By Felix Naumann, Melanie Herschel, M. Tamer Ozsu

With the ever expanding quantity of information, information caliber difficulties abound. a number of, but diversified representations of a similar real-world items in info, duplicates, are probably the most fascinating info caliber difficulties. the results of such duplicates are dangerous; for example, financial institution consumers can receive replica identities, stock degrees are monitored incorrectly, catalogs are mailed a number of occasions to an analogous loved ones, and so on. instantly detecting duplicates is tough: First, replica representations will not be exact yet a bit vary of their values. moment, in precept all pairs of files might 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 immediately establish duplicates while evaluating files. Well-chosen similarity measures enhance the effectiveness of reproduction detection. (ii) Algorithms are constructed to accomplish on very huge volumes of knowledge in look for duplicates. Well-designed algorithms enhance the potency of reproduction detection. eventually, we talk about the right way to assessment the good fortune of reproduction detection. desk of Contents: facts detoxification: creation and Motivation / challenge Definition / Similarity services / reproduction Detection Algorithms / comparing Detection luck / end and Outlook / Bibliography

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From now on, we illustrate only the case where tokens arise from tokenizing string values, but readers should keep in mind that this is not the only possible use of the token-based measures we discuss. Assuming we tokenize two strings s1 and s2 using a tokenization function tokenize(· ), the question arises how we build the two vectors V and W from the tokens. We discuss the solution in two steps: first, we discuss the dimensionality d of these two vectors before we focus on what numbers are filled in these vectors.

3) whereas the second variant computes the similarity of candidates. String comparison based on the Jaccard coefficient. Given a tokenization function tokenize(s) that tokenizes a string s into a set of string tokens {s1 , s2 , . . 1. TOKEN-BASED SIMILARITY 25 Candidate comparison based on the Jaccard coefficient. 1 that these are compared based on their respective object description. 3) Consider a scenario where Person is a candidate type. , the Name attribute. Hence, Person candidates have a description attribute Name.

The intuition behind the inverse document frequency is that it assigns higher weights to tokens that occur less frequently in the scope of all candidate descriptions. , in a database listing insurance companies, the token Insurance is likely to occur very frequently across object descriptions and the idf thus assigns it a lower weight than to more distinguishing tokens such as Liberty or Prudential. 6) As a reminder, n is the total number of candidates. We compute the tf-idf score for every token ti ∈ D and set the i-th value in the term vector T to this score.

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