Advertisement

The Use of Reference Graphs in the Entity Resolution of Criminal Networks

  • David RobinsonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)

Abstract

Entity resolution (ER) is the detection of duplicated records within a dataset representing the same real-world entity. The importance of ER is amplified within law enforcement as criminal data, or criminal networks, has inherent uncertainty and ER inaccuracy incurs a high cost. Commercial ER solutions focus on fast and scalable resolution of obvious pairs of entities, rather than the more complex non-obvious pairs which are so critical to law enforcement. Here we outline the use of proper names represented as reference graphs - generated from an algorithm that conducts name similarity, logic-based pruning, and classification using community detection and a proper name origin algorithm. The resultant classes are used at indexing and decision management stages within an ER model to support the detection of non-obvious duplicate entities. Utility is clearly demonstrated through the application of the approach on three real-world datasets of varying origin, size, topology, and heterogeneity.

Keywords

Entity resolution Record linkage Reference graph Criminal networks Indexing Decision management Community detection 

References

  1. 1.
    Benjelloun, O., Garcia-Molina, H., Menestrina, D., Su, Q., Whang, S.E., Widom, J.: Swoosh: a generic approach to entity resolution. VLDB J. 18(1), 255–276 (2009)CrossRefGoogle Scholar
  2. 2.
    Maeno, Y.: Node discovery problem for a social network. Connections 29, 62–76 (2009)Google Scholar
  3. 3.
    Odell, M., Russell, R.: The Soundex Coding System. US Patents 1261167 (1918)Google Scholar
  4. 4.
    Philips, L.: The double metaphone search algorithm. C/C ++ Users J. 18(6), 38–43 (2000)MathSciNetGoogle Scholar
  5. 5.
    Philips, L.: Metaphone 3 version 2.5.4 (2015)Google Scholar
  6. 6.
    de Vries, T., Ke, H., Chawla, S., Christen, P.: Robust record linkage blocking using suffix arrays. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 305–314. ACM (2009)Google Scholar
  7. 7.
    Hernández, M.A., Stolfo, S.J.: The merge/purge problem for large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data 1995, pp. 127–138. ACM, New York (1995)Google Scholar
  8. 8.
    Hernández, M.A., Stolfo, S.J.: Real-world data is dirty: data cleansing and the merge/purge problem. Data Min. Knowl. Discov. 2(1), 9–37 (1998)CrossRefGoogle Scholar
  9. 9.
    McCallum, A., Nigam, K., Ungar, L.H.: Efficient clustering of high-dimensional data sets with application to reference matching. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 169–178. ACM (2000)Google Scholar
  10. 10.
    Taylor, J.: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. Pearson Education, Boston (2012)Google Scholar
  11. 11.
    Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Trans. Knowl. Discov. Data 1(1), 1–36 (2007)CrossRefGoogle Scholar
  12. 12.
    Bhattacharya, I., Getoor, L.: Entity Resolution in Graphs. In: Cook, D.J., Holder, L.B. (eds.) Mining Graph Data, pp. 311–344. Wiley, Hoboken (2006)CrossRefGoogle Scholar
  13. 13.
    Köpcke, H., Rahm, E.: Frameworks for entity matching: a comparison. Data Knowl. Eng. 69(2), 197–210 (2010)CrossRefGoogle Scholar
  14. 14.
    Randall, S.M., Boyd, J.H., Ferrante, A., Bauer, J.K., Semmens, J.B.: Use of graph theory measures to identify errors in record linkage. Comput. Methods Programs Biomed. 115(2), 55–63 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhou, Y., Talburt, J.R.: Strategies for large-scale entity resolution based on inverted index data partitioning. In: Yeoh, W., Talburt, J.R., Zhou, Y. (eds.) Information Quality and Governance for Business Intelligence, pp. 329–351. IGI Global, Hershey (2013)Google Scholar
  16. 16.
    Michalowski, M., Thakkar, S., Knoblock, C.A.: Exploiting secondary sources for unsupervised record linkage. In: Proceedings of the 30th VLDB Conference, Toronto, Canada (2004)Google Scholar
  17. 17.
    Papadakis, G., Koutrika, G., Palpanas, T., Nejdl, W.: Meta-blocking: taking entity resolution to the next level. IEEE Trans. Knowl. Data Eng. 26(8), 1946–1960 (2014)CrossRefGoogle Scholar
  18. 18.
    Winkler, W.E.: String comparator metrics and enhanced decision rules in the fellegi-sunter model of record linkage. In: Proceedings of the Section on Survey Research Methods, American Statistical Association, pp. 354–359 (1990)Google Scholar
  19. 19.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exper. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  20. 20.
    Ferrante, A., Boyd, J.: A transparent and transportable methodology for evaluating data linkage software. J. Biomed. Inform. 45(1), 165–172 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Inland RevenueWellingtonNew Zealand

Personalised recommendations