A Comparison of String Metrics for Matching Names and Records
By William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg. 2003
601 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/994006685f312459b9985902584d31205254f0d2
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- Parag Singla
- Iryna Gurevych
- Pedro M. Domingos
- Torsten Zesch
- Hoifung Poon
- Daniel Bär
- Giorgos Stoilos
- Chi-Kin Chau
Citations in the corpus
- Entity Resolution with Markov Logic (2006, background): “[6] found such hybrid measures to outperform pure word-based and pure string-based ones for entity resolution.”
- Automatic HVAC Control with Real-time Occupancy Recognition and Simulation-guided Model Predictive Control in Low-cost Embedded System (2017, methodology): “Since it is often possible to have a naive classifier that has a high Precision but a low Recall or vice versa, a better metric called F1 - Score has been utilized in [44, 45], which is basically a harmonic mean of Precision and Recall .”
- DKPro Similarity: An Open Source Framework for Text Similarity (2013, methodology): “SecondString Toolkit The freely available library by Cohen et al. (2003)12 is similar to SimMetrics, and also implemented in Java.”
- A Survey of Schema-Based Matching Approaches (2005, unknown): “Usually, the similarity comparison between a pair of nodes from the two schemas/ontologies is based on the analysis of their positions within the graphs.”
- A String Metric for Ontology Alignment (2005, methodology): “The second one is performed with classical benchmarks found in literature for data integration and retrieval [20].”