Noise-Tolerant Approximate Blocking for Dynamic Real-Time Entity Resolution

  • Huizhi Liang
  • Yanzhe Wang
  • Peter Christen
  • Ross Gayler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8444)

Abstract

Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach.

Keywords

Entity Resolution Real-time Locality Sensitive Hashing Indexing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Christen, P.: Data Matching. Data-Centric Systems and Appl. Springer (2012)Google Scholar
  2. 2.
    Christen, P., Gayler, R.W.: Adaptive temporal entity resolution on dynamic databases. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 558–569. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Lange, D., Naumann, F.: Cost-aware query planning for similarity search. Information Systems, 455–469 (2012)Google Scholar
  4. 4.
    Bhattacharya, I., Getoor, L., Licamele, L.: Query-time entity resolution. In: SIGKDD, pp. 529–534 (2006)Google Scholar
  5. 5.
    Christen, P., Gayler, R., Hawking, D.: Similarity-aware indexing for real-time entity resolution. In: CIKM, pp. 1565–1568 (2009)Google Scholar
  6. 6.
    Ramadan, B., Christen, P., Liang, H., Gayler, R.W., Hawking, D.: Dynamic similarity-aware inverted indexing for real-time entity resolution. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013 Workshops. LNCS (LNAI), vol. 7867, pp. 47–58. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: VLDB, pp. 518–529 (1999)Google Scholar
  8. 8.
    Kim, H.S., Lee, D.: HARRA: Fast iterative hashed record linkage for large-scale data collections. In: EDBT, pp. 525–536 (2010)Google Scholar
  9. 9.
    Bawa, M., Condie, T., Ganesan, P.: LSH forest: Self-tuning indexes for similarity search. In: WWW, pp. 651–660 (2005)Google Scholar
  10. 10.
    Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe LSH: Efficient indexing for high-dimensional similarity search. In: VLDB, pp. 950–961 (2007)Google Scholar
  11. 11.
    Das Sarma, A., Jain, A., Machanavajjhala, A., Bohannon, P.: An automatic blocking mechanism for large-scale de-duplication tasks. In: CIKM, pp. 1055–1064 (2012)Google Scholar
  12. 12.
    Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: Scene: A scalable two-stage personalized news recommendation system. In: SIGIR, pp. 125–134 (2011)Google Scholar
  13. 13.
    Anand, R., Ullman, J.D.: Mining of massive datasets. Cambridge University Press (2011)Google Scholar
  14. 14.
    Gan, J., Feng, J., Fang, Q., Ng, W.: Locality-sensitive hashing scheme based on dynamic collision counting. In: SIGMOD, pp. 541–552 (2012)Google Scholar
  15. 15.
    Michelson, M., Knoblock, C.A.: Learning blocking schemes for record linkage. In: AAAI, pp. 440–445 (2006)Google Scholar
  16. 16.
    Yan, S., Lee, D., Kan, M.Y., Giles, L.C.: Adaptive sorted neighborhood methods for efficient record linkage. In: DL, pp. 185–194 (2007)Google Scholar
  17. 17.
    Draisbach, U., Naumann, F., Szott, S., Wonneberg, O.: Adaptive windows for duplicate detection. In: ICDE, pp. 1073–1083 (2012)Google Scholar
  18. 18.
    Christen, P.: Preparation of a real voter data set for record linkage and duplicate detection research. Technical report, Australian National University (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Huizhi Liang
    • 1
  • Yanzhe Wang
    • 1
  • Peter Christen
    • 1
  • Ross Gayler
    • 2
  1. 1.Research School of Computer ScienceThe Australian National UniversityCanberraAustralia
  2. 2.VedaMelbourneAustralia

Personalised recommendations