1. A Comparison of String Matching Distance Metrics for Name-Matching Tasks William Cohen, Pradeep RaviKumar, Stephen Fienberg

2. Motivating Example List of people and some attributes compiled by one source Updates by another source need to be merged Need to locate matching records Forcing exact match not sufficient –Typographical errors (letter “B” vs. letter “V”) –Scanning errors (letter “I” vs. numeral “1”) –Such errors exceed 20% in some cases Decide when two records match  Decide when two strings (or words) are identical

3. History – String Matching Statistics –Treat as a classification problem [Fellegi & Sunter] –Use of other prior knowledge String represented as a feature vector Databases –No prior knowledge Use of distance functions – edit distance, Monge & Elkan, TFIDF –Knowledge-intensive approaches User interaction [Hernandez & Stolfo] Artificial Intelligence –Learn the parameters of the edit distance functions –Combine the results of different distance functions Compare string matching distance functions for the task of name matching

4. Edit Distance Number of edit operations needed to go from string s to string t Operations: insert, delete, substitution Levenstein: assigns unit cost –Distance (“smile”, “mile”) = 1 –Distance (“meet”, “meat”) = 1 Computed by dynamic programming Reordering of words can be misleading –“Cohen, William” vs. “William Cohen”

5. Edit Distance Monger-Elkan: assigns relatively lower cost to sequence of insertions or deletions –A + B*(n – 1) for n insertions or deletions (B < A) Other methods that assign decreasing costs to subsequent insertions

6. Edit Distance Jaro (s, t) –s’ be characters in s common with t –t’ be characters in t common with s –T (s’, t’) be half the number of transpositions in for s’ and t’

7. Improvements to Jaro McLaughlin –Exact match – weight of 1.0 –Similar characters – weight of 0.3 Scanning error (“I” vs. “1”) Typographical error (“B” vs. “V”) Pollock and Zamora –Error rates increase as the position in string moves to the right –Adjust output of Jaro by fixed amount depending upon how many of the first 4 characters match

8. Term Based Treat strings s & t as bags S and T of words Examples –Jaccard similarity = |S∩T| / |SUT| –TFIDF

9. Term Based Words may be weighted to make the common words count less Advantages –Exploits frequency information –Ordering of words doesn’t matter (Cohen, William vs. William Cohen) Disadvantages –Sensitive to errors in spelling (Cohen vs. Cohon) and abbreviations (Univ. vs. University) –Ordering of words ignored (City National Bank vs. National City Bank)

10. Hybrid Distance Functions Recursive Matching –Let s = (a 1, a 2, … a K ) and t = (b 1, b 2, …, b L ) –Sim’ is the level two matching function

11. Blocking / Pruning Methods Comparing all pairs – too expensive when lists are large A pair (s, t) is a candidate for match if they share some substring v that appears in at most a fraction f of all names Using a v of length 4 and f = 1% finds on an average of 99% correct pairs

12. Results - Metric Output of each algorithm is a list of candidate pairs ranked by distance Non-interpolated average precision of a ranking Other metrics used –Interpolated precision

13. Results - Matching Term based: TFIDF most accurate Edit distance based: Monge-Elkan most accurate Jaro as accurate as Monge-Elkan, but much faster Combine TFIDF and Jaro