Adaptive Name Matching in Information Integration

By Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg. IEEE Intelligent Systems, 2003

561 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/aa2bd932a2ecb6e07c768bcf0dc119f0cd20f6e0

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Duplicate Record Detection: A Survey (2007, background): “In the database community, the problem is described as merge-purge [14], data deduplication [15], and instance identification [16]; in the AI community, the same problem is described as database hardening [17] and name matching [18].”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Another related problem is record linkage [34], [35], [36], [37], [38], [39], [40], [41] (also called duplicate detection, entity matching, and reference reconciliation) in the database community.”
Name disambiguation in author citations using a K-way spectral clustering method (2005, methodology): “Name matching [9, 11, 13, 45] usually identiﬁes a name entity with different name labels from duplicate records of different syntactic formats.”
An Introduction to Duplicate Detection (2010, methodology): “Throughout the duplicate detection literature, we observe two basic uses of the Jaccard coefficient: tokens are either obtained from strings [Monge and Elkan, 1996], or they correspond to complete descriptions [Bilenko et al., 2003].”
Data integration: the teenage years (2006, background): “[16] Mikhail Bilenko, Raymond Mooney, William Cohen, Pradeep Ravikumar, and Stephen Fienberg.”
Efficient similarity joins for near duplicate detection (2008, background): “Near Duplicate Object Detection Near duplicate object detection has been studied under different names in several areas, including record linkage [27], merge-purge [19], data deduplication [23], name matching [4], just to name a few.”
Improving the Study of Campaign Contributors with Record Linkage (2010, methodology): “Several solutions have been proposed, including blocking, sorted neighborhood, clustering, canopies and set joins (Bilenko et al. 2003).”
Mining knowledge from text using information extraction (2005, background): “Identifying textually distinct items that refer to the same entity is an instance of the general database “deduping” or record-linkage problem [42; 5].”
Two supervised learning approaches for name disambiguation in author citations (2004, background): “Much research has been done to address the identity uncertainty problem in different fields using different methods, such as record linkage [17], duplicate record detection and elimination [8, 26, 29], merge/purge [22], data association [6], database hardening [11], citation matching [28], name matc”
Collective entity resolution in relational data (2007, methodology): “For comparing attributes, which is required for all of the algorithms, we use the Soft TF-IDF similarity for names [Cohen et al. 2003; Bilenko et al. 2003] since it has been shown to perform well for name-based entity resolution.”
Annotating and searching web tables using entities, types and relationships (2010, methodology): “We can also use a number of other similarity measures, such as Jaccard or a soft cosine measure [2].”
Reference reconciliation in complex information spaces (2005, methodology): “To date, most reference reconciliation approaches (see [20, 4] for recent surveys) employ approximate string similarity measures and combine the similarities of multiple attributes.”
Privacy-Preserving Record Linkage (2010, background): “When fields are strings such as names and addresses, then string edit distances [21, 2] are useful.”