A Comparison of String Matching Distance Metrics for Name-Matching Tasks

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 = (a1, a2, … aK) and t = (b1, b2, …, bL) • 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