A Comparison of String Distance Metrics for Name-Matching Tasks

By William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg. 2003

1,755 citations recorded by the source corpus.

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Citations in the corpus

SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “This problem could be addressed by using more involved string similarity measures as described in [10], though our current implementation only uses (6) for simplicity.”
A cost-based model and effective heuristic for repairing constraints by value modification (2005, methodology): “While we use a relatively simple similarity function in our experiments, our goal is to allow a variety of attribute-similarity metrics (see [ 8 , 28]) from the data linkage community to be applied.”
A Comparison of Personal Name Matching: Techniques and Practical Issues (2006, methodology): “This will also limit the use of adaptive name matching algorithms [7] that are trained to deal with certain types of variations and errors.”
Efficient Top-k Query Evaluation on Probabilistic Data (2007, background): “There is a rich literature on record linkage (also known as de-duplication, or merge-purge) [1, 4, 9, 11, 14, 15, 22, 23], that offers an excellent collection of techniques for computing these similarity scores.”
OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services (2009, methodology): “Based on the experimental results of measuring the performance of similarity metrics for text information retrieval provided by Cohen and his colleagues [3], we selected the top performing ones to build the OWLS-MX variants.”
Management of probabilistic data: foundations and challenges (2007, background): “This is a central problem in data cleaning and data integration, and has also been called record linkage, deduplication, or merge-purge [34, 3, 13, 37, 43, 49, 15, 80, 7].”
Collective entity resolution in relational data (2007, methodology): “There has been extensive work on de(cid:12)ning approximate string similarity measures [Monge and Elkan 1996; Navarro 2001; Cohen et al. 2003; Chaudhuri et al. 2003] that may be used for unsupervised entity resolution.”
Learning Entailment Relations by Global Graph Structure Optimization (2012, methodology): “Then we compute the Levenshtein string edit-distance (Cohen, Ravikumar, and Fienberg 2003) between s 1 and s 2 and divide the score by | s 1 | + | s 2 | for normalization.”
The stringdist Package for Approximate String Matching (2014, positive): “Winkler (1990) and Cohen et al. (2003) use a value of p = 0.”
Automated semantic web service discovery with OWLS-MX (2006, methodology): “Based on the experimental results of measuring the performance of similarity metrics for text information retrieval provided by Cohen and his colleagues [4], we selected the top performing ones to build the OWLS-MX variants.”
Improving the Study of Campaign Contributors with Record Linkage (2010, background): “Experience does suggest that Monge-Elkan, Jaro-Winkler and Soundex are well suited for name matching (Pfeifer et al. 1996; Cohen et al. 2003).”
Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA (2015, background): “It is rather intended for short strings and therefore suits our aim to detect duplicate authors (Cohen et al., 2003).”
Large-Scale Collective Entity Matching (2011, neutral): “, using approximate string-matching techniques) [17, 5, 2].”
A Comparison of String Metrics for Matching Names and Records (2003, methodology): “In previous work ( Cohen, Ravikumar, & Fienberg 2003 ), we used this toolkit to conduct a comparison of several string distances on the tasks of matching and clustering lists of entity names.”
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (2012, background): “While statisticians and health researchers commonly name the task of matching records as data or record linkage [11] (the term used in this paper), the computer science and database communities refer to the same process as data or field matching [12], data integration [13], data scrubbing or cleanin”