Learning domain-independent string transformation weights for high accuracy object identification

By Sheila Tejada, Craig A. Knoblock, Steven N. Minton. Knowledge Discovery and Data Mining, 2002

321 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8ff529534f4f83f93a1d0a100d089f2042e53f3c

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In Information Systems

In Management Science and Operations Research

Citations in the corpus

Adaptive Name Matching in Information Integration (2003, background): “Recently, machine learning methods that utilize supervision in the form of matched and unmatched record pairs for training classifiers to distinguish between them have been proposed, including those that try to select most informative record pairs for human labeling to produce maximum accuracy impro”
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”
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (2012, background): “Many of them are based on machine learning approaches [10], [20], [28], [29], [30], [31].”
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.”
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions (2006, methodology): “Active learning is used in [41] and [ 46 ] to address the problem of lack of training data.”
Reference reconciliation in complex information spaces (2005, methodology): “as record linkage [37], merge/purge [21], de-duplication [34], hardening soft databases [9], reference matching [27], object identification [35] and identity uncertainty [26].”
Data Matching (2012, methodology): “Such interactive approaches are known as active learning [11, 231, 252].”
Frameworks for entity matching: A comparison (2010, methodology): “, Naïve Bayes [49], logistic regression [46], Support Vector Machine (SVM) [11,43,49] or decision trees [63,29,49,53,54,56] have so far been used for some subtasks, e.”
Adaptive Blocking: Learning to Scale Up Record Linkage (2006, methodology): “For example, in record linkage (also known as object identification [35], de-duplication [31], entity matching [9, 33, 2] and identity uncertainty [29, 23]), similarity must be computed between all pairs of records to identify groups of records that refer to the same underlying entity.”
Duplicate Record Detection: A Survey (2007, background): “The key innovation in this work is the creation of several redundant functions and the concurrent exploitation of their con icting actions in order to discover new kinds of inconsistencies among duplicates in the data set.”
Entity Resolution with Markov Logic (2006, background): “Several authors have devised , compared and learned similarity measures for use in entity resolution (e.g., [6] [45] [3]).”