Skip to main content
SpringerLink
Log in

Hybrid entity clustering using crowds and data

  • Special Issue Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Query result clustering has attracted considerable attention as a means of providing users with a concise overview of results. However, little research effort has been devoted to organizing the query results for entities which refer to real-world concepts, e.g., people, products, and locations. Entity-level result clustering is more challenging because diverse similarity notions between entities need to be supported in heterogeneous domains, e.g., image resolution is an important feature for cameras, but not for fruits. To address this challenge, we propose a hybrid relationship clustering algorithm, called Hydra, using co-occurrence and numeric features. Algorithm Hydra captures diverse user perceptions from co-occurrence and disambiguates different senses using feature-based similarity. In addition, we extend Hydra into \({\mathsf{Hydra }_\mathsf{gData }}\) with different sources, i.e., entity types and crowdsourcing. Experimental results show that the proposed algorithms achieve effectiveness and efficiency in real-life and synthetic datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. www.freebase.com.

  2. www.mpi-inf.mpg.de/yago-naga/yago.

  3. We adopt refined \(R_{ij}\) from [53] to discourage an extreme case of merging two distance clusters with large size difference, i.e., \(|C_{i_1}| \gg |C_{i_2}|\). More details on this refined notion can be found in [53].

  4. These entity types in Table 1 are collected from Freebase (www.freebase.com).

References

  1. Aggarwal, C.C.: A human-computer cooperative system for effective high dimensional clustering. In: KDD (2001)

  2. Aggarwal, C.C., Wolf, J.L., Yu, P.S., Procopiuc, C., Park, J.S.: Fast algorithms for projected clustering. In: SIGMOD (1999)

  3. Aggarwal, C.C., Yu, P.S.: Finding generalized projected clusters in high dimensional spaces. In: SIGMOD (2000)

  4. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high-dimensional data for a data mining applications. In: SIGMOD (1998)

  5. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)

  6. Ananthakrishna, R., Chaudhuri, S., Ganti, V.: Eliminating fuzzy duplicates in data warehouses. In: VLDB, pp. 586–597 (2002)

  7. Arasu, A., Götz, M., Kaushik, R.: On active learning of record matching packages. In: SIGMOD Conference, pp. 783–794 (2010)

  8. Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: KDD, pp. 59–68 (2004)

  9. Bazzanella, B., Stoermer, H., Bouquet, P.: Entity type disambiguation in user queries. JIKM 10(3), 209–224 (2011)

    Google Scholar 

  10. Bilenko, M., Basu, S., Sahami, M.: Adaptive product normalization: Using online learning for recored linkage in comparison shopping. In: ICDM (2005)

  11. Bouquet, P., Palpanas, T., Stoermer, H., Vignolo, M.: A conceptual model for a web-scale entity name system. In: ASWC, pp. 46–60 (2009)

  12. Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: CIKM, pp. 1287–1296 (2009)

  13. Cheng, C.-H., Fu, A.W., Zhang, Y.: Entropy-based subspace clustering for mining numerical data. In: KDD (1999)

  14. Cheng, D., Kannan, R., Vempala, S., Wang, G.: A divide-merge methodology for clustering. In: TODS (2005)

  15. Chierichetti, F., Kumar, R., Pandey, S., Vassilvitskii, S.: Finding the jaccard median. In: SODA, pp. 293–311 (2010)

  16. Cohen, W.W.: Integration of heterogeneous databases without common domains using queries based on textual similarity. In: SIGMOD, pp. 201–212 (1998)

  17. Cui, Y., Hasler, N., Thormählen, T., Seidel, H.-P.: Scale invariant feature transform with irregular orientation histogram binning. In: ICIAR, pp. 258–267 (2009)

  18. Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Commun. ACM 54(4), 86–96 (2011)

    Article  Google Scholar 

  19. Franklin, M.J., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: CrowdDB: answering queries with crowdsourcing. In: SIGMOD, pp. 61–72 (2011)

  20. Goil, S., Nagesh, H., Choudhary, A.: Mafia: efficient and scalable subspace clustering for very large data sets. Technical Report, Northwesthen University (1999)

  21. Gomes, R., Welinder, P., Krause, A., Perona, P.: Crowdclustering. In: NIPS, pp. 558–566 (2011)

  22. Hearst, M.A., Pedersen, J.O.: Re-examining the cluster hypothesis: Scatter/Gather on retrieval results. In: SIGIR (1996)

  23. Jain, A., Pennacchiotti, M.: Open entity extraction from web search query logs. In: COLING, pp. 510–518 (2010)

  24. Jang, M., Park, J.-W., Hwang, S.: Predictive mining of comparable entities from the web. In: AAAI (2012)

  25. Ji, X., Xu, W., Zhu, S.: Document clustering with prior knowledge. In: SIGIR (2006)

  26. Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: SIGIR, pp. 244–251 (2006)

  27. Lee, J., Hwang, S., Nie, Z., Wen, J.-R.: Query result clustering for object-level search. In: KDD, pp. 1205–1214 (2009)

  28. Lee, J., Hwang, S., Nie, Z., Wen, J.-R.: Navigation system for product search. In: ICDE, pp. 1113–1116 (2010)

  29. Lee, T., Wang, Z., Wang, H., Hwang, S.: Web scale taxonomy cleansing. PVLDB 4(12), 1295–1306 (2011)

    Google Scholar 

  30. Li, S., Lin, C.-Y., Song, Y.-I., Li, Z.: Comparable entity mining from comparative questions. In: ACL, pp. 650–658 (2010)

  31. Liu, Y., Li, W., Lin, Y., Jing, L.: Spectral geometry for simultaneously clustering and ranking query search results. In: SIGIR (2008)

  32. Marcus, A., Wu, E., Madden, S., Miller, R.C.: Crowdsourced databases: Query processing with people. In: CIDR, pp. 211–214 (2011)

  33. Mecca, G., Raunich, S., Pappalardo, A.: A new algorithm for clustering search results. Data Knowl. Eng. 62(3), 504–522 (2007)

    Google Scholar 

  34. Nie, Z., Ma, Y., Shi, S., Wen, J.-R., Ma, W.-Y.: Web object retrieval. In: WWW (2007)

  35. Nie, Z., Wen, J.-R., Ma, W.-Y.: Object-level vertical search. In: CIDR (2007)

  36. Nie, Z., Wen, J.-R., Ma, W.-Y.: Statistical entity extraction from the web. Proc. IEEE 100(9), 2675–2687 (2012)

    Google Scholar 

  37. Nie, Z., Zhang, Y., Wen, J.-R., Ma, W.-Y.: Object-level ranking: bringing order to web objects. In: WWW (2005)

  38. Parameswaran, A.G., Polyzotis, N.: Answering queries using humans, algorithms and databases. In: CIDR, pp. 160–166 (2011)

  39. Parsons, L., Haque, E., Liu, H.: Subspace clustering for high dimensional data: a review. SIGKDD Newsletter 6(1), 90–105 (2004)

    Article  Google Scholar 

  40. Patrikainen, A., Melia, M.: Comparing subspace clusterings. TKDE 18(7), 902–916 (2006)

    Google Scholar 

  41. Radlinski, F., Dumais, S.T.: Improving personalized web search using result diversification. In: SIGIR, pp. 691–692 (2006)

  42. Scripps, J., Tan, P.-N.: Clustering in the presence of bridge-nodes. In: SDM (2006)

  43. Selke, J., Lofi, C., Balke, W.-T.: Pushing the boundaries of crowd-enabled databases with query-driven schema expansion. PVLDB 5(6), 538–549 (2012)

    Google Scholar 

  44. Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: IJCAI, pp. 2330–2336 (2011)

  45. Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Conttrainted k-means clustering with background knowledge. In: ICML (2001)

  46. Wang, J., Kraska, T., Franklin, M.J., Feng, J.: CrowdER: crowdsourcing entity resolution. PVLDB 5(11), 1483–1494 (2012)

    Google Scholar 

  47. Wang, X., Zhai, C.: Learn from web search logs to organize search results. In: SIGIR (2007)

  48. Wang, X.-J., Ma, W.-Y., He, Q.-C., Li, X.: Grouping web image search result. In: ACM Multimedia, pp. 436–439 (2004)

  49. Whang, S.E., Benjelloun, O., Garcia-Molina, H.: Generic entity resolution with negative rules. VLDB J. 18(6), 1261–1277 (2009)

    Article  Google Scholar 

  50. Whang, S.E., Lofgren, P., Garcia-Molina, H.: Question selection for crowd entity resolution. In: PVLDB (2013)

  51. Woo, K.-G., Lee, J.-H., Kim, M.-H., Lee, Y.-J.: FINDIT: a fast intelligent subspace clusteing algorithm using diemsnion voting. Inform. Softw. Technol. 46(4), 255–271 (2004)

    Article  Google Scholar 

  52. Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: SIGIR (2003)

  53. Yip, K.Y., Cheung, D.W., Ng, M.K.: HARP: A practical projected clustering algorithm. TKDE 16(11), 1387–1397 (2004)

    Google Scholar 

  54. Yip, K.Y., Cheung, D.W., Ng, M.K.: On discovery of extremely low-dimensional clusters using semi-supervised projected clustering. In: ICDE (2005)

  55. Zamir, O., Etzioni, O.: Web document clustering: a feasibility demonstration. In: SIGIR (1998)

  56. Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., Ma, J.: Learning to cluster web search results. In: SIGIR (2004)

  57. Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, pp. 912–919 (2003)

Download references

Acknowledgments

This research was supported by the Ministry of Knowledge Economy (MKE), Korea and Microsoft Research, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency). (NIPA-2012-H0503-12-1036).

Author information

Authors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Jongwuk Lee, Hyunsouk Cho, Jin-Woo Park, Young-rok Cha & Seung-won Hwang

  2. Microsoft Research Asia, Beijing, People’s Republic of China

    Zaiqing Nie

  3. Renmin University of China, Beijing, People’s Republic of China

    Ji-Rong Wen

Authors
  1. Jongwuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyunsouk Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin-Woo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Young-rok Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seung-won Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zaiqing Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ji-Rong Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seung-won Hwang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, J., Cho, H., Park, JW. et al. Hybrid entity clustering using crowds and data. The VLDB Journal 22, 711–726 (2013). https://doi.org/10.1007/s00778-013-0328-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-013-0328-8

Keywords

Search

Navigation