Learning object identification rules for information integration

By Sheila Tejada, Craig A. Knoblock, Steven N. Minton. Information Systems, 2001

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a95903c7c35bd830c1f226b21c6bbbf7c46b5374

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In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

A Comparison of String Metrics for Matching Names and Records (2003, methodology): “Original sources are 1. (Cohen 2000) 2. ( Tejada, Knoblock, & Minton 2001 ) 3. (Monge & Elkan 1996) 4. William Winkler (personal communication)”
Collective entity resolution in relational data (2007, methodology): “Other approaches use active learning [Sarawagi and Bhamidipaty 2002; Tejada et al. 2001] where the user is asked to label ambiguous examples by the learner.”
Joint Entity Resolution (2012, background): “Entity Resolution has been studied under various names including record linkage [17], merge/purge [12], deduplication [18], reference reconciliation [8], object identification [19], and more (see [21, 11] for recent surveys).”
Entity resolution with iterative blocking (2009, background): “Entity Resolution has been studied under various names including record linkage [19], merge/purge [12], deduplication [20], reference reconciliation [7], object identiﬁcation [21], and others (see [8, 23, 9] for recent surveys).”
Frameworks for entity matching: A comparison (2010, methodology): “Four of the training-based frameworks expect the users to provide suitable training data manually, while Active Atlas [ 53 ,54], MARLIN [11,12], STEM [36] and FEBRL [18,17] have also investigated (semi-)automatic training methods.”
Entity Resolution with Markov Logic (2006, background): “Another has focused on the use of active learning techniques to minimize the need for labeled data (e.g., [44] [38] [4]).”
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.”
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005, background): “There has been extensive work on approximate string matching algorithms [26, 8] and adaptive algorithms that learn string similarity measures [4, 9, 33].”
A String Metric for Ontology Alignment (2005, methodology): “Thus we could not resist but to evaluate it with classical benchmarks found in literature like the ones in [7,8,24,20].”
Learning domain-independent string transformation weights for high accuracy object identification (2002, methodology): “We have developed an object identification system called Active Atlas [18], which applies a set of general string transformations in order to propose possible mappings between the objects and then employs an active learning technique that learns the necessary mapping rules to classify the mappings w”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Active learning techniques alleviate one major drawback of the supervised learning techniques, which may require a large training data set, by actively soliciting user feedback for unlabeled record pairs, which, when labeled, will provide the highest utility to the learner [108, 96, 5].”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Entity resolution (also known as record linkage [42], object identiﬁcation [43], instance matching [44], and deduplication [45]) is the problem of identifying which objects in relational data refer to the same underlying entities.”
Overview and Framework for Data and Information Quality Research (2009, methodology): “Entity resolution[Wang and Madnick 1989; Talburt et al. 2005], also known as record linkage [Winkler 2006] and object identi.cation [Tejada et al. 2001], provides techniques for identifying data records pertaining to the same entity.”
An interactive clustering-based approach to integrating source query interfaces on the deep Web (2004, positive): “We apply the following normalizations [18, 30].”