Entity resolution with iterative blocking

By Steven Euijong Whang, David Menestrina, Georgia Koutrika, Martin Theobald, Hector Garcia-Molina. 2009

247 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Giorgio Patrini
2. Peter Christen
3. Richard Nock
4. Stephen Hardy
5. Erhard Rahm
6. Tibério S. Caetano
7. Hector Garcia-Molina
8. Minos N. Garofalakis

In Computer Networks and Communications

1. Peter Christen
2. Giorgio Patrini
3. Erhard Rahm
4. Hector Garcia-Molina
5. Minos N. Garofalakis
6. Nilesh N. Dalvi
7. Hanna Köpcke
8. Steven Euijong Whang

In Management Science and Operations Research

1. Peter Christen
2. Giorgio Patrini
3. Richard Nock
4. Stephen Hardy
5. Erhard Rahm
6. Tibério S. Caetano
7. Hector Garcia-Molina
8. Minos N. Garofalakis

Citations in the corpus

• Fast Learning from Distributed Datasets without Entity Matching (2016, methodology): “By analogy with entity resolution [Whang et al., 2009], we define block rados as rados, lifted to X, that are sums of edges matching a particular signature and class in all peers.”
• A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (2012, methodology): “The first category is to develop new and improve existing techniques with the aim of making them more scalable to large data sets while keeping a high linkage quality [22], [33], [40], [41], [43], [48], [50], [51].”
• Large-Scale Collective Entity Matching (2011, background): “[8] have proposed an iterative blocking framework, where the key idea is to allow blocks to communicate their “local” EM results in an iterative manner (until a fixpoint is reached).”
• Frameworks for entity matching: A comparison (2010, methodology): “Overlapping methods may result in overlapping blocks of entities; implementations include the (multi-pass) sorted neighborhood approach [33], bi-gram indexing [4], canopy clustering [40] and iterative blocking [59].”
• Data Matching (2012, methodology): “An iterative blocking technique has recently been proposed that uses the information gained when records within a block are compared to improve the records contained in other blocks [277].”
• Joint Entity Resolution (2012, background): “Although several recent works [17], [18] have proposed general scalable ER algorithms for a single entity type of data, they do not discuss”