Learning to match and cluster large high-dimensional data sets for data integration

By William W. Cohen, Jacob Richman. Knowledge Discovery and Data Mining, 2002

452 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/51321a60f5ec2c80253394ef86e8b5fcc768f52a

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In Information Systems

In Management Science and Operations Research

In Signal Processing

Citations in the corpus

Learning similarity metrics for event identification in social media (2010, methodology): “In domains where more than one similarity metric is appropriate, several approaches have been proposed for combining multiple similarities using machine learning techniques [8, 10, 12, 13].”
Distributed Representations of Tuples for Entity Resolution (2017, methodology): “Popular approaches include SVM [7], active learning [45], clustering [12].”
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions (2006, methodology): “Many linkage methods based on machine learning, artificial intelligence and information retrieval techniques have been developed in the past few years [6, 10, 11, 20, 21, 22 , 25, 27, 34, 35, 38, 41, 46, 50, 53].”
A Comparison of String Metrics for Matching Names and Records (2003, methodology): “Minton 2001; Cohen & Richman 2002; Bilenko & Mooney 2002) we also used a learning scheme to combine several of the distance functions above.”
Data Matching (2012, methodology): “Various machine learning techniques have been investigated, both unsupervised and supervised [31, 59, 85, 102].”
A Comparison of Fast Blocking Methods for Record Linkage (2003, methodology): “Recent work by McCallum, Nigam and Unger[7], Cohen and Richman[2], and others have proposed the use of high-dimensional similarity indexing to improve the efficiency of blocking methods.”
Adaptive duplicate detection using learnable string similarity measures (2003, background): “Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications -Data Mining; I.2.6 [Arti.cial Intelligence]: Learning Keywords Data cleaning, record linkage, distance metric learning, trained similarity measures, string edit distance, SVM applications 1.”
A taxonomy of privacy-preserving record linkage techniques (2013, methodology): “Approximate matching as well as accurate classification techniques are needed to achieve accurate linkage quality in record linkage applications [5,16].”
Reference reconciliation in complex information spaces (2005, methodology): “In order to demonstrate the applicability of our algorithm in a more conventional setting, our second experiment uses the subset of the Cora dataset provided by McCallum [11] and used previously in [8, 3, 30].”
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.”
Collective entity resolution in relational data (2007, methodology): “Another approach is to use adaptive supervised algorithms that learn string similarity measures from labeled data [Ristad and Yianilos 1998; Bilenko and Mooney 2003; Cohen and Richman 2002; Tejada et al. 2001].”
A Comparison of String Distance Metrics for Name-Matching Tasks (2003, methodology): “Following previous researchers (Tejada, Knoblock, & Minton 2001; Cohen & Richman 2002; Bilenko & Mooney 2002) we used a learning scheme to combine several of the distance functions above.”
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].”
Entity Resolution with Markov Logic (2006, background): “One line of research has focused on scaling entity resolution to large databases by avoiding the quadratic number of comparisons between all pairs of entities (e.g., [20] [30] [26] [7]).”
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (2012, methodology): “This indexing technique is based on the idea of using a computationally cheap clustering approach to create highdimensional overlapping clusters, from which blocks of candidate record pairs can then be generated [29], [44].”
Deep entity matching with pre-trained language models (2020, background): “EM solutions have tackled the blocking problem [2, 6, 14, 28, 45] and the matching problem with rules [9, 13, 38, 44], crowdsourcing [16, 18, 43], or machine learning [37, 8, 3, 16, 20].”