Progressive approach to relational entity resolution

Proceedings of the VLDB Endowment

Volumn 7, Issue 11, 2014, Pages 999-1010

  Altowim, Yasser ^a   Kalashnikov, Dmitri V ^a   Mehrotra, Sharad ^a  

^a Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL EVALUATIONS; ENTITY RESOLUTIONS; RESOLUTION COSTS; TRADE OFF;

ECONOMIC AND SOCIAL EFFECTS;

EID: 84904649403     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/2732967.2732975     Document Type: Conference Paper

References (24)

1. http://ics.uci.edu/~yaltowim/ProgER.pdf.
2. http://csxstatic.ist.psu.edu/about/data.
3. H. Altwaijry, D. V. Kalashnikov, and S. Mehrotra. Query-driven approach to entity resolution. In VLDB, pp. 1846-1857, 2013.
4. R. Ananthakrishna, S. Chaudhuri, and V. Ganti. Eliminating fuzzy duplicates in data warehouses. In VLDB, pp. 586-597, 2002.
5. O. Benjelloun, H. Garcia-Molina, D. Menestrina, Q. Su, S. E. Whang, and J. Widom. Swoosh: a generic approach to entity resolution. VLDB J., pp. 255-276, 2009.
6. I. Bhattacharya and L. Getoor. Collective entity resolution in relational data. TKDD, pp. 1-36, 2007.
7. J. J. Burg, J. Ainsworth, B. Casto, and S.-D. Lang. Experiments with the oregon trail knapsack problem. Electronic Notes in Discrete Mathematics, 1:26-35, 1999.
8. Z. Chen, D. V. Kalashnikov, and S. Mehrotra. Exploiting context analysis for combining multiple entity resolution systems. In SIGMOD, pp. 207-218, 2009.
9. R. Cheng, E. Lo, X. S. Yang, M.-H. Luk, X. Li, and X. Xie. Explore or exploit?: effective strategies for disambiguating large databases. In VLDB, pp. 815-825, 2010.
10. G. F. Cooper. The computational complexity of probabilistic inference using bayesian belief networks. Articial intelligence, 42(2):393-405, 1990.
11. A. Culotta and A. McCallum. Joint deduplication of multiple record types in relational data. In CIKM, pp. 257-258, 2005.
12. X. Dong, A. Halevy, and J. Madhavan. Reference reconciliation in complex information spaces. In SIGMOD, pp. 85-96, 2005.
13. M. Henrion. Practical issues in constructing a bayes' belief network. In UAI, pp. 132-139, 1987.
14. M. Hernandez and S. Stolfo. The merge/purge problem for large databases. In SIGMOD, pp. 127-138, 1995.
15. S. R. Jeffery, M. J. Franklin, and A. Y. Halevy. Pay-as-you-go user feedback for dataspace systems. In SIGMOD, pp. 847-860, 2008.
16. F. V. Jensen and T. D. Nielsen. Bayesian networks and decision graphs. Springer, 2007.
17. J. F. Lemmer and D. E. Gossink. Recursive noisy or - a rule for estimating complex probabilistic interactions. Trans. Sys. Man Cyber. Part B, pp. 2252-2261, 2004.
18. A. K. McCallum, K. Nigam, and L. Ungar. Effcient clustering of high-dimensional data sets with application to reference matching. In SIGKDD, pp. 169-178, 2000.
19. R. Nuray-Turan, D. V. Kalashnikov, and S. Mehrotra. Exploiting web querying for web people search. In TODS, pp. 7:1-7:41, 2012.
20. J. Pearl. Probabilistic reasoning in intelligent systems:networks of plausible inference. Morgan Kaufmann, 1988.
21. J. Wang, T. Kraska, M. J. Franklin, and J. Feng. Crowder:Crowdsourcing entity resolution. In VLDB, pp. 1483-1494, 2012.
22. S. Whang, D. Marmaros, and H. Garcia-Molina. Pay-as-you-go entity resolution. In TKDE, pp. 1111-1124, 2013.
23. S. E. Whang and H. Garcia-Molina. Joint entity resolution. In ICDE, pp. 294-305, 2012.
24. M. Yakout, A. K. Elmagarmid, H. Elmelegy, M. Ouzzani, and A. Qi. Behavior based record linkage. In VLDB, pp. 439-448, 2010.