Record Linkage Survey

1. Record Linkage Survey Tan Yee Fan 2007 February 9 WING Group Meeting

2. Contents • Introduction • Record linkage using internal knowledge • String matching • Classification or clustering • Graphical formalisms • Blocking • Record linkage using search engine • Adaptive methods • Conclusion

3. Ambiguous representation of named entities Citation records Web pages People names Products Customer records Images … Merging information from various sources Typos? Errors? Think of large scale databases of millions of records! Introduction

4. Commercial world • Addresses • Dongwon Lee, 110 E. Foster Ave. #410, State College, PA, 16802 • LEE Dong, 110 East Foster Avenue Apartment 410, University Park, PA 16802-2343 • Products • Honda Fix vs. Honda Jazz • T-Fal vs. Tefal • Apple iPod Nano 4GB vs. 4GB iPod nano 4GB Examples courtesy of Dongwon Lee (Penn State University)

5. Author names and citations Jeffrey D. Ullman (Stanford University) Some images courtesy of Dongwon Lee(Penn State University)

6. Web pages Note: I did not author any of these web pages!

7. More on person names • Highly ambiguous • Only 90,000 different names for 100 million people (U.S. Census Bureau) • Valid changes: • Customs: Lee, Dongwon vs. Dongwon Lee vs. LEE Dongwon • Marriage: Carol Dusseau vs. Carol Arpaci-Dusseau • Misc.: Sean Engelson vs. Shlomo Argamon Examples courtesy of Dongwon Lee (Penn State University)

8. Record linkage • Input • Two lists of records, A and B • Output • For each record a in A and for each record b in B,does a and b refer to the same entity? • Note • Entities do not come with unique identifiers • To disambiguate (deduplicate) items in a single list L, we set A = B = L

9. Fellegi-Sunter model no-decision region (hold for human review) designate as definite non-match designate as definite match * true matches○ true non-matches false non-matches false matches sim(a, b)

10. Contents • Introduction • Record linkage using internal knowledge • String matching • Classification or clustering • Graphical formalisms • Blocking • Record linkage using search engine • Adaptive methods • Conclusion

11. String matching • String similarity • Strings as ordered sequences • Edit distance • Jaro and Jaro-Winkler • Strings as unordered sets • Jaccard similarity • Cosine similarity • Abbreviation matching • Usually pattern detection in texts • e.g. “Almost Locked Sets (ALS)” ([a], [b], [c]) ≠ ([c], [b], [a]) {[a], [b], [c]} = {[c], [b], [a]}

12. Classification or clustering • Feature engineering + model selection • Features • String similarity, relationships (e.g. collaborators) • Models • Naïve Bayes, Support Vector Machine, K-means, Agglomerative Clustering, … Yoojin Hong, Byung-Won On and Dongwon Lee. SystemSupport for Name Authority Control Problem inDigital Libraries: OpenDBLP Approach. ECDL 2004. Sudha Ram, Jinsoo Park and Dongwon Lee. DigitalLibraries for the Next Millennium: Challenges andResearch Directions. Information Systems Frontiers 1999.

13. Classification or clustering • Graphical models • Structure • Nodes: Record fields or entire records • Edges: Join fields with similar values, fields to records, etc… • Characteristic • Model global knowledge • Propagate information around the graph until convergence • Usually very time consuming • Examples • Conditional random field • Dependency graph • Generative probabilistic model

14. J. C. Latombe T.-H. Chiang D. Hsu A. Dhanik Y. Wang L. Qiu M.-Y. Kan Y. F. Tan H. Cui T.-S. Chua Social network analysis • Social network • Nodes: entities (e.g. author names) • Edges: relationships (e.g. coauthored a paper) coauthorship network

15. J. C. Latombe T.-H. Chiang D. Hsu A. Dhanik Y. Wang L. Qiu M.-Y. Kan Y. F. Tan H. Cui T.-S. Chua Social network analysis • Analysis • Connected components • Distance between nodes • Node/edge centrality • Cliques • Bipartite subgraphs • …

16. Connected triple Random walk Maximum flow Clustering Social network analysis x1 s t x2 x2 x1 x3

17. Pairwise comparisons Requires O(n2) time Major bottleneck Possible solutions Blocking techniques Avoiding pairwise comparisons altogether Scalability Issues Input: d1, d2, …, dn for i = 1 to n for j = (i + 1) to n compute sim(di, dj)

18. Contents • Introduction • Record linkage using internal knowledge • String matching • Classification or clustering • Graphical formalisms • Blocking • Record linkage using search engine • Adaptive methods • Conclusion

19. Record linkage using search engine • Previously… • We assumed input data records contain sufficient information to perform linkage • What if… • There is insufficient or only noisy information? • e.g. linking short forms to long forms • Ask other people! • Use web as collective knowledge of people

20. Record linkage using search engine Number of results Ranked list Title Snippet URL Web page sudoku strategiessudoku OR strategies “sudoku strategies”

21. Counts Co-occurrence measure between count(q), count(q’) andcount(q and q’) Hostnames from URLs Overlap between the hostnames of results of queries q and q’ Inverse Host Frequency Snippets or web pages Cosine similarity using the tokens Counts of specific terms e.g. number of snippets for the query q containing the string q’ Do natural language processing Examples

22. Suppose e and e’ refer to the same entity Then web pages of e and web pages of e’ are likely to share some representative data “Jeffrey D. Ullman”384,000 pages “Jeffrey D. Ullman” + “aho”174,000 pages “J. Ullman”124,000 pages “J. Ullman” + “aho”41,000 pages “Shimon Ullman”27,300 pages “Shimon Ullman” + “aho”66 pages Googled name linkage

23. Query probing Googling and web page downloadsare expensive on time! • Consider • Joint Conference on Digital Libraries • European Conference on Digital Libraries • Digital Libraries • Query probing • Use common n-gram “digital libraries” as query probe • If we can obtain information on all three conferences, we save two queries

24. Methods Ms: stronger method but very slow (e.g. web page similarity) Mw: weaker method but fast (e.g. host overlap) Aim Accuracy close to Ms Significantly reduced running time than Ms Algorithm Execute Mw If heuristic suggests that Mw results are likely incorrect Execute Ms Adaptive querying

25. Comment • These techniques • Blocking • Query probing • Adaptive querying • Combine different methods to obtain the better aspects of each

26. Contents • Introduction • Record linkage using internal knowledge • String matching • Classification or clustering • Graphical formalisms • Blocking • Record linkage using search engine • Adaptive methods • Conclusion

27. Conclusion • Comment • This survey is very brief and broad, but still many aspects not covered • History • Record linkage became a research issue in the 1940s, possibly due to analysis of census data or medical records • Research directions • Graphical models utilize global knowledge, but how to make them scalable for large datasets • Utilizing external knowledge in an effective and scalable manner • Adaptive methods

28. Thank You

29. Selected Bibliography • General and surveys • Ivan P. Fellegi and Alan B. Sunter. A theory for record linkage. Journal of the American Statistical Association, 64(328):1183–1210, December 1969. • William E. Winkler and Yves Thibaudeau. An application of the Fellegi-Sunter Model of record linkage to the 1990 U.S. Decennial Census. Technical Report RR91/09, U.S. Bureau of the Census, 1991. • Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios. Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering (TKDE), 19(1):1–16, January 2007. • William E. Winkler. Overview of record linkage and current research directions. Technical Report RRS2006/02, U.S. Bureau of the Census, February 2006. • Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. Adaptive name matching in information integration. IEEE Intelligent Systems, 18(5):16–23, January/February 2003. • Min-Yen Kan and Yee Fan Tan. Record Matching in Digital Library Metadata. To appear in Communications of the ACM (CACM).

30. Selected Bibliography • String matching • Robert A. Wagner and Michael J. Fischer. The string-to-string correction problem. Journal of the Association of Computing Machinery, 21(1):168–173, January 1974. • Saul B. Needleman and Christian D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 148(3):443–453, March 1970. • Temple F. Smith and Michael S. Waterman. Identification of common molecular subsequences. Journal of Molecular Biology, 147(1):195–197, March 1981. • Andrés Marzal and Enrique Vidal. Computation of normalized edit distance and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):926–932, September 1993. • Alvaro E. Monge and Charles Elkan. The field matching problem: Algorithms and applications. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 267–270, August 1996. • Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004. • Mikhail Bilenko and Raymond J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 39–48, August 2003. • Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005. • William. W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. A comparison of string distance metrics for name-matching tasks. In Information Integration on the Web (IIWeb), pages 73–78, August 2003. • Ariel S. Schwartz and Marti A. Hearst. A simple algorithm for identifying abbreviation definitions in biomedical text. In Pacific Symposium on Biocomputing (PSB), pages 451–462, January 2003. • Youngja Park and Roy J. Byrd. Hybrid text mining for finding abbreviations and their definitions. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 126–133, June 2001. • Jeffrey T. Chang , Hinrich Schütze, and Russ B. Altman. Creating an online dictionary of abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 9(6):612–620, November/December 2002. • Hiroko Ao and Toshihisa Takagi. ALICE: An algorithm to extract abbreviations from MEDLINE. Journal of the American Medical Informatics Association, 12(5):576–586, September/October 2005.

31. Selected Bibliography • Direct classification or clustering, and blocking • Hui Han, Hongyuan Zha, and C. Lee Giles. A model-based K-means algorithm for name disambiguation. In Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data, October 2003. • Hui Han, C. Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. Two supervised learning approaches for name disambiguation in author citations. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 296–305, June 2004. • Hui Han, Wei Xu, Hongyuan Zha, and C. Lee Giles. A hierarchical naive bayes mixture model for name disambiguation in author citations. In ACM Symposium on Applied Computing (SAC), pages 1065–1069, March 2005. • Hui Han, Hongyuan Zha, and C. Lee Giles. Name disambiguation in author citations using a K-way spectral clustering method. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 334–343, June 2005. • Dongwon Lee, Byung-Won On, Jaewoo Kang, and Sanghyun Park. Effective and scalable solutions for mixed and split citation problems in digital libraries. In ACM SIGMOD Workshop on Information Quality in Information Systems (IQIS), pages 69–76, June 2005. • Byung-Won On, Dongwon Lee, Jaewoo Kang, and Prasenjit Mitra. Comparative study of name disambiguation problem using a scalable blocking-based framework. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 344–353, June 2005. • Andrew McCallum, Kamal Nigam, and Lyle Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 169–178, August 2000. • Matthew Michelson and Craig A. Knoblock. Learning blocking schemes for record linkage. In National Conference on Artificial Intelligence (AAAI), July 2006. • Mikhail Bilenko, Beena Kamath, and Raymond J. Mooney. Adaptive Blocking: Learning to Scale Up Record Linkage and Clustering. In IEEE International Conference on Data Mining (ICDM), December 2006.

32. Selected Bibliography • Graphical models • Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311–321, March 2004. • John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning (ICML), pages 282–289, June/July 2001. • Andrew McCallum and Ben Wellner. Object consolidation by graph partitioning with a conditionally-trained distance metric. In ACM SIGKDD Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pages 19–24, August 2003. • Ben Wellner, Andrew McCallum, Fuchun Peng, and Michael Hay. An integrated, conditional model of information extraction and coreference with application to citation matching. In Conference on Uncertainty in Artificial Intelligence (UAI), pages 593–601, July 2004. • Andrew McCallum, Kedar Bellare, and Fernando Pereira. A Conditional Random Field For Discriminatively-Trained Finite-State String Edit Distance. In Conference on Uncertainty in Artificial Intelligence (UAI), July 2005. • Xin Dong, Alon Halevy, and Jayant Madhavan. Reference reconciliation in complex information spaces. In ACM SIGMOD International Conference on Management of Data, pages 85–96, June 2005. • Indrajit Bhattacharya and Lise Getoor. A latent dirichlet model for unsupervised entity resolution. In SIAM International Conference on Data Mining, pages 47–58, April 2006.

33. Selected Bibliography • Social network analysis • H. A. Kautz, B. Selman, and M. A. Shah. The hidden web. AI Magazine, 18(2):27–36, 1997. • P. Mutschke. Mining networks and central entities in digital libraries. A graph theoretic approach applied to co-author networks. In Intelligent Data Analysis (IDA), pages 155–166, August 2003. • M. E. J. Newman. Who is the best connected scientist? A study of scientific coauthorship networks. In Complex Networks, pages 337–370, February 2004. • E. Otte and R. Rousseau. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6), December 2002. • T. Krichel and N. Bakkalbasi. A social network analysis of research collaboration in the economics community. In International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, May 2006. • R. Rousseau and M. Thelwall. Escher staircases on the world wide web. First Monday, 9(6), June 2004. • D. G. Feitelson. On identifying name equivalences in digital libraries. Information Research, 9(4), October 2004. • R. Bekkerman and A. McCallum. Disambiguating web appearances of people in a social network. In International conference on World Wide Web (WWW), pages 463–470, May 2005. • R. Holzer, B. Malin, and L. Sweeney. Email alias detection using social network analysis. In Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), August 2005. • B. Malin, E. Airoldi, and K. M. Carley. A network analysis model for disambiguation of names in lists. Computational and Mathematical Organization Theory, 11(2):119–139, July 2005. • G. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 150–160, August 2000. • P. K. Reddy and M. Kitsuregawa. An approach to build a cyber-community hierarchy. In SIAM ICDM Workshop on Web Analysis, April 2002. • Patrick Reuther. Personal name matching: New test collections and a social network based approach. Technical Report Mathematics/Computer Science 06-01, University of Trier, March 2006. • Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397-406, May 2006.

34. Selected Bibliography • Web-based methods • Jamie P. Callan, Margie E. Connell, and Aiqun Du. Automatic discovery of language models for text databases. In ACM SIGMOD International Conference on Management of Data, pages 479–490, June 1999. • Jamie P. Callan and Margie E. Connell. Query-based sampling of text databases. ACM Transactions on Information Systems (TOIS), 19(2):97–130, April 2001. • Panagiotis G. Ipeirotis and Luis Gravano. Distributed search over the hidden-web: Hierarchical database sampling and selection. In International Conference on Very Large Databases (VLDB), pages 394–405, August 2002. • Luis Gravano, Panagiotis G. Ipeirotis, and Mehran Sahami. QProber: A system for automatic classification of hidden-web databases. ACM Transactions on Information Systems (TOIS), 21(1):1–41, January 2003. • Aron Culotta, Ron Bekkerman, and Andrew McCallum. Extracting social networks and contact information from email and the web. In Conference on Email and Anti-Spam (CEAS), July 2004. • Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In International conference on World Wide Web (WWW), pages 462–471, May 2004. • Philipp Cimiano, Günter Ladwig, and Steffen Staab. Gimme the context: Context-driven automatic semantic annotation with C-PANKOW. In International conference on World Wide Web (WWW), pages 332–341, May 2005. • Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Keisuke Ishida, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, and Mitsuru Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In International conference on World Wide Web (WWW), pages 397-406, May 2006. • Yee Fan Tan, Min-Yen Kan, and Dongwon Lee. Search engine driven author disambiguation. In ACM/IEEE Joint Conference on Digital Libraries (JCDL), June 2006. • Ergin Elmacioglu, Min-Yen Kan, Dongwon Lee, and Yi Zhang. Googled name linkage. 2007. • Yee Fan Tan, Ergin Elmacioglu, Min-Yen Kan, and Dongwon Lee. Record Linkage of Short Forms to Long Forms: A Case Study of Publication Venues. 2007. • Min-Yen Kan. Web page classification without the web page. In International conference on World Wide Web (WWW), pages 262–263, May 2004. • Min-Yen Kan and Hoang Oanh Nguyen Thi. Fast webpage classification using url features. In International Conference on Information and Knowledge Management (CIKM), pages 325–326, October/November 2005. • Panagiotis G. Ipeirotis, Eugene Agichtein, Pranay Jain, and Luis Gravano. To search or to crawl? Towards a query optimizer for text-centric tasks. In ACM SIGMOD International Conference on Management of Data, pages 265–276, June 2006.