Advertisement

Diversified Semantic Query Reformulation

  • Rubén ManriqueEmail author
  • Olga Mariño
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)

Abstract

One main challenge for search engines is retrieving the user’s intended results. Diversification techniques are employed to cover as many aspects of the query as possible through a tradeoff between the relevance of the results and the diversity in the result set. Most diversification techniques reorder the final result set. However, these diversification techniques could be inadequate for search scenarios with small candidate set sizes, or those for which response time is a critical issue. This paper presents a diversification technique for such scenarios. Instead of reordering the result set, the query is reformulated, thus taking advantage of the knowledge available in Linked Data Knowledge Bases. The query is annotated with semantic data and then expanded to related resources. An adapted Maximal Marginal Relevance technique is applied to select resources from this expanded set whose properties form the expanded query. Experiments conducted on federated and non-federated scenarios show that this method has superior diversification capacity and shorter response times than algorithms based on result set reordering.

Notes

Acknowledgment

This work was partially supported by COLCIENCIAS PhD scholarship (Call 647-2014).

References

  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 5–14, WSDM 2009. ACM, New York (2009)Google Scholar
  2. 2.
    Bouchoucha, A., He, J., Nie, J.Y.: Diversified query expansion using conceptnet. In: Proceedings of the 22nd ACM International Conference on Information Knowledge Management, CIKM 2013, pp. 1861–1864. ACM, New York (2013)Google Scholar
  3. 3.
    Bouchoucha, A., Liu, X., Nie, J.Y.: Integrating Multiple Resources for Diversified Query Expansion, pp. 437–442. Springer, Cham (2014)Google Scholar
  4. 4.
    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, SIGIR 1998, pp. 335–336. ACM, New York (1998)Google Scholar
  5. 5.
    Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. 44(1), 1–50 (2012)CrossRefzbMATHGoogle Scholar
  6. 6.
    Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 121–124 (2013)Google Scholar
  7. 7.
    Dang, V., Croft, W.B.: Diversity by proportionality: an election-based approach to search result diversification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2012, p. 65 (2012)Google Scholar
  8. 8.
    Ghansah, B., Wu, S.: A mean-variance analysis based approach for search result diversification in federated search. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 24(02), 195–211 (2016)CrossRefGoogle Scholar
  9. 9.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: Proceedings of the 18th International Conference on World Wide Web, pp. 381–390, WWW 2009 (2009)Google Scholar
  10. 10.
    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, p. 851 (2012)Google Scholar
  11. 11.
    Hong, D., Si, L.: Search result diversification in resource selection for federated search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 613–622 (2013)Google Scholar
  12. 12.
    Kapanipathi, P., Jain, P., Venkataramani, C.: Hierarchical interest graph. Technical report (2015)Google Scholar
  13. 13.
    Minack, E., Demartini, G., Nejdl, W.: Current approaches to search result diversification. In: Proceedings of 1st International Workshop on Living Web (2009)Google Scholar
  14. 14.
    Paul, C., Rettinger, A., Mogadala, A., Knoblock, C.A., Szekely, P.: Efficient graph-based document similarity. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 334–349. Springer, Cham (2016). doi: 10.1007/978-3-319-34129-3_21 CrossRefGoogle Scholar
  15. 15.
    Pekar, V., Staab, S.: Taxonomy learning: factoring the structure of a taxonomy into a semantic classification decision. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7 (2002)Google Scholar
  16. 16.
    Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, p. 781 (2010)Google Scholar
  17. 17.
    Rubien, R., Ziak, H., Kern, R.: Efficient search result diversification via query expansion using knowledge bases. In: Proceedings of 12th International Workshop on Text-based Information Retrieval (TIR), p. 5 (2015)Google Scholar
  18. 18.
    Santos, R.L.T., Macdonald, C., Ounis, I.: Aggregated search result diversification. In: Amati, G., Crestani, F. (eds.) ICTIR 2011. LNCS, vol. 6931, pp. 250–261. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23318-0_23 CrossRefGoogle Scholar
  19. 19.
    Santos, R.L.T., Macdonald, C., Ounis, I.: On the role of novelty for search result diversification. Inf. Retrieval 15(5), 478–502 (2012)CrossRefGoogle Scholar
  20. 20.
    Vargas, S., Castells, P., Vallet, D.: Explicit relevance models in intent-oriented information retrieval diversification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, p. 75 (2012)Google Scholar
  21. 21.
    Vee, E., Srivastava, U., Shanmugasundaram, J., Bhat, P., Yahia, S.A.: Efficient computation of diverse query results. In: Proceedings - International Conference on Data Engineering, pp. 228–236 (2008)Google Scholar
  22. 22.
    Vieira, M.R., Razente, H.L., Barioni, M.C.N., Hadjieleftheriou, M., Srivastava, D., Traina, C., Tsotras, V.J.: On query result diversification. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, pp. 1163–1174, ICDE 2011. IEEE Computer Society, Washington, DC (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Systems and Computing Engineering Department, School of EngineeringUniversidad de Los AndesBogotáColombia

Personalised recommendations