Evaluation of entity resolution approaches on real-world match problems

By Hanna Köpcke, Andreas Thor, Erhard Rahm. Proceedings of the VLDB Endowment, 2010

531 citations recorded by the source corpus.

Subfields: Epidemiology, Management Science and Operations Research, Oral Surgery

View Paper: https://www.semanticscholar.org/paper/cf6c8ce4ec5bbbae9be4b9076c41d49a8eaad7b3

How researchers see this paper

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

In Epidemiology

1. Peter Christen
2. AnHai Doan

In Management Science and Operations Research

1. Sanjay Krishnan
2. M. Franklin
3. Jiannan Wang
4. Ken Goldberg
5. Eugene Wu
6. Tim Kraska
7. Tova Milo
8. Ihab F. Ilyas

In Oral Surgery

1. Peter Christen
2. AnHai Doan

Citations in the corpus

• A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “For instance, an evaluation of the popular duplicate detection technique [9] shows that the matching phase takes on the order of minutes for a dataset of thousands of tuples [39].”
• CrowdER: Crowdsourcing Entity Resolution (2012, methodology): “learning-based technique. First, we computed a feature vector for each pair of records. For the Restaurant dataset, we chose two similarity functions, edit distance and cosine similarity , adopted by [18], and computed their values on four attributes to obtain a 8-dimensional feature vector; For the”
• Joint Entity Resolution (2012, background): “Most of the ER works [15] have focused on resolving one dataset.”
• Deep entity matching with pre-trained language models (2020, methodology): “We present the experiment results on benchmark datasets for EM: the ER Benchmark datasets [21], the Magellan datasets [20] and the WDC product data corpus [31].”
• Distributed Representations of Tuples for Entity Resolution (2017, methodology): “Our experimental setup was an adaptation of prior ER evaluations methods [31, 32, 47] to handle distributed representations.”
• Deep Learning for Entity Matching: A Design Space Exploration (2018, methodology): “The first 7 are smaller datasets from [15] except for "Amazon-Google" which is from [43].”
• Data Matching (2012, methodology): “recently discussed the issues related to evaluating data matching systems in more detail [169].”