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International Conference on Web Information Systems Engineering

WISE 2016: Web Information Systems Engineering – WISE 2016 pp 157–173Cite as

Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10041)

Abstract

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.

Keywords

  • Query Expansion
  • Entity Recommendation
  • Wikipedia Entities
  • Vertex-reinforced Random Walk (VRRW)
  • Search Result Diversification (SRD)

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-48740-3_11
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Fig. 1.
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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Pythonidae.

  2. 2.

    https://en.wikipedia.org/wiki/Monty_Python.

  3. 3.

    https://en.wikipedia.org/wiki/Python_(programming_language).

  4. 4.

    Terms may have associated weights.

  5. 5.

    https://en.wikipedia.org/wiki/Jaguar.

  6. 6.

    http://www.jaguar.co.uk/.

  7. 7.

    https://en.wikipedia.org/wiki/Jaguar_Racing.

  8. 8.

    http://www.retrogamer.net/profiles/hardware/atari-jaguar-2/.

  9. 9.

    http://www.jaguars.com/.

  10. 10.

    The other option, using sum instead of max, could cause some highly connected nodes in \(N_2\) to have much higher weights than those in \(N_1\).

  11. 11.

    P. Onca is the scientific name of the wild cat called Jaguar.

  12. 12.

    https://sites.google.com/site/slrcompanion2016/.

References

  1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  2. Bouchoucha, A., He, J., Nie, J.Y.: Diversified query expansion using conceptnet. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, pp. 1861–1864. ACM (2013)

    Google Scholar 

  3. Bouchoucha, A., Liu, X., Nie, J.-Y.: Integrating multiple resources for diversified query expansion. In: Rijke, M., Kenter, T., Vries, A.P., Zhai, C.X., Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 437–442. Springer, Heidelberg (2014). doi:10.1007/978-3-319-06028-6_38

    CrossRef  Google Scholar 

  4. Bouchoucha, A., Liu, X., Nie, J.-Y.: Towards query level resource weighting for diversified query expansion. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 1–12. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16354-3_1

    Google Scholar 

  5. Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM (1998)

    Google Scholar 

  6. Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Dexter 2.0 - an open source tool for semantically enriching data. In: Proceedings of the ISWC 2014 Posters and Demonstrations Track a Track within the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, October 21, 2014, pp. 417–420 (2014)

    Google Scholar 

  7. Clueweb: (2009). http://lemurproject.org/clueweb09/

  8. Collins-Thompson, K.: Estimating robust query models with convex optimization. In: Advances in Neural Information Processing Systems, pp. 329–336 (2009)

    Google Scholar 

  9. Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 365–374. ACM (2014)

    Google Scholar 

  10. Deepak, P., Ranu, S., Banerjee, P., Mehta, S.: Entity linking for web search queries. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 394–399. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16354-3_43

    Google Scholar 

  11. Dou, Z., Hu, S., Chen, K., Song, R., Wen, J.R.: Multi-dimensional search result diversification. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 475–484. ACM (2011)

    Google Scholar 

  12. Ferragina, P., Scaiella, U.: Tagme: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1625–1628. ACM (2010)

    Google Scholar 

  13. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)

    Google Scholar 

  14. He, B., Ounis, I.: Combining fields for query expansion and adaptive query expansion. Inf. Process. Manage. 43(5), 1294–1307 (2007)

    CrossRef  Google Scholar 

  15. He, J., Hollink, V., de Vries, A.: Combining implicit and explicit topic representations for result diversification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 851–860. ACM (2012)

    Google Scholar 

  16. Jakarta, A.: Apache lucene-a high-performance, full-featured text search engine library (2004)

    Google Scholar 

  17. Liu, X., Bouchoucha, A., Sordoni, A., Nie, J.Y.: Compact aspect embedding for diversified query expansions. Proc. AAAI 14, 115–121 (2014)

    Google Scholar 

  18. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. In: Proceedings of the 7th International World Wide Web Conference, pp. 161–172 (1998)

    Google Scholar 

  19. Pemantle, R.: Vertex-reinforced random walk. Probab. Theor. Relat. Fields 92(1), 117–136 (1992)

    MathSciNet  CrossRef  MATH  Google Scholar 

  20. Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, pp. 881–890. ACM (2010)

    Google Scholar 

  21. Santos, R.L.T., Peng, J., Macdonald, C., Ounis, I.: Explicit search result diversification through sub-queries. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 87–99. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12275-0_11

    CrossRef  Google Scholar 

  22. Schuhmacher, M., Ponzetto, S.P.: Knowledge-based graph document modeling. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 543–552. ACM (2014)

    Google Scholar 

  23. Singh, A., Raghu, D., et al.: Retrieving similar discussion forum threads: a structure based approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 135–144. ACM (2012)

    Google Scholar 

  24. Song, R., Luo, Z., Wen, J.R., Yu, Y., Hon, H.W.: Identifying ambiguous queries in web search. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1169–1170. ACM (2007)

    Google Scholar 

  25. Strohman, T., Metzler, D., Turtle, H., Croft, W.B.: Indri: A language model-based search engine for complex queries. In: Proceedings of the International Conference on Intelligent Analysis. vol. 2, pp. 2–6. Citeseer (2005)

    Google Scholar 

  26. Vargas, S., Santos, R.L., Macdonald, C., Ounis, I.: Selecting effective expansion terms for diversity. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 69–76 (2013)

    Google Scholar 

  27. Whissell, J.S., Clarke, C.L.: Improving document clustering using okapi bm25 feature weighting. Inf. Retr. 14(5), 466–487 (2011)

    CrossRef  Google Scholar 

  28. Xu, Y., Jones, G.J., Wang, B.: Query dependent pseudo-relevance feedback based on wikipedia. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 59–66. ACM (2009)

    Google Scholar 

  29. Zhu, X., Goldberg, A.B., Van Gael, J., Andrzejewski, D.: Improving diversity in ranking using absorbing random walks. In: HLT-NAACL, pp. 97–104. Citeseer (2007)

    Google Scholar 

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Authors and Affiliations

  1. IIT Madras, Chennai, India

    Adit Krishnan & Sayan Ranu

  2. Queen’s University Belfast, Belfast, Northern Ireland, UK

    Deepak Padmanabhan

  3. IBM Research, New Delhi, India

    Sameep Mehta

Authors
  1. Adit Krishnan
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  2. Deepak Padmanabhan
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  3. Sayan Ranu
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  4. Sameep Mehta
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Corresponding author

Correspondence to Adit Krishnan .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Krishnan, A., Padmanabhan, D., Ranu, S., Mehta, S. (2016). Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-48740-3_11

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