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Exploring Context-Sensitive Query Reformulation in a Biomedical Digital Library

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Digital Libraries: Providing Quality Information (ICADL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9469))

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Abstract

We propose a novel semantic query expansion technique that enables inference of contextual information in queries and user information. In the present study, we detect and map bio entities such as gene, protein, and disease in a query to concept tuples, and incorporate user context data based on the PubMed query logs and user profile into the algorithm. In objective evaluation, we can see a concept tuple aided with UMLS concepts adds semantic information to the initial query. In subjective evaluation, we find that in a context-enabled search environment, where context terms that the users are interested in are combined into their initial search terms, users tend to assign higher relevance scores to the retrieval results by these queries.

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References

  1. Bai, J., Nie, J.Y., Bouchard, H., Cao, G.: Using query contexts in information retrieval. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 15–22 (2007)

    Google Scholar 

  2. Greenberg, J.: Optimal query expansion (QE) processing methods with semantically encoded structured thesauri terminology. J. Am. Soc. Inf. Sci. Technol. 52(6), 487–498 (2001)

    Article  Google Scholar 

  3. Shamim Khan, M., Sebastian, S.: Enhanced web document retrieval using automatic query expansion. J. Am. Soc. Inf. Sci. Technol. 55(1), 29–40 (2004)

    Article  Google Scholar 

  4. Stokes, N., Li, Y., Cavedon, L., Zobel, J.: Exploring criteria for successful query expansion in the genomic domain. Inf. Retr. Boston 12(1), 17–50 (2009)

    Article  Google Scholar 

  5. Xu, X., Zhu, W., Zhang, X., Hu, X., Song, I.Y.: A comparison of local analysis, global analysis and ontology-based query expansion strategies for bio-medical literature search. In: IEEE International Conference on Systems, Man and Cybernetics (SMC 2006), vol. 4, pp. 3441–3446 (2006)

    Google Scholar 

  6. Srinivasan, P.: Query expansion and MEDLINE. Inf. Process. Manag. 32(4), 431–443 (1996)

    Article  Google Scholar 

  7. Matos, S., Arrais, J.P., Maia-Rodrigues, J., Oliveira, J.L.: Concept-based query expansion for retrieving gene related publications from MEDLINE. BMC Bioinformatics, 11, 212 (2010)

    Google Scholar 

  8. Leroy, G., Chen, H.: Meeting medical terminology needs-the ontology-enhanced medical concept mapper. IEEE J. Biomed. Health. Inform. 5(4), 261–270 (2001)

    Google Scholar 

  9. Yang, Y., Chute, C.G.: An application of least squares fit mapping to text information retrieval. In: ACM SIGIR conference on Research and Development in Information Retrieval, pp. 281–290 (1992)

    Google Scholar 

  10. Aronson, A.R., Rindflesch, T.C.: Query expansion using the UMLS metathesaurus. In: Proc. AMIA. Symp., pp. 485–489 (1997)

    Google Scholar 

  11. Díaz-Galiano, M.C., Martín-Valdivia, M.T., Ureña-López, L.A.: Query expansion with a medical ontology to improve a multimodal information retrieval system. Comput. Biol. Med. 39(4), 396–403 (2009)

    Article  Google Scholar 

  12. Lu, Z., Kim, W., Wilbur, W.J.: Evaluation of query expansion using MeSH in PubMed. Inf. Retr. Boston 12(1), 69–80 (2009)

    Article  Google Scholar 

  13. Jain, H., Thao, C., Zhao, H.: Enhancing electronic medical record retrieval through semantic query expansion. Information Systems and E-Business Management 10(2), 165–181 (2012)

    Article  Google Scholar 

  14. Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim, E., Ngo, L., Dibble, E.: Assisting consumer health information retrieval with query recommendations. J. Am. Med. Inform. Assoc. 13(1), 80–90 (2006)

    Article  Google Scholar 

  15. Mosa, A.S.M., Yoo, I.: A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log. BMC Med. Inform. Decis. Mak. 13(1), 8 (2013)

    Article  Google Scholar 

  16. Billerbeck, B., Scholer, F., Williams, H.E., Zobel, J.: Query expansion using associated queries. In: Proc. ACM Int. Conf. Inf. Knowl. Manag., pp. 2–9 (2003)

    Google Scholar 

  17. Cui, H., Wen, J.R., Nie, J.Y., Ma, W.Y.: Probabilistic query expansion using query logs. In: Proc. Int. World Wide Web Conf., pp. 325–332 (2002)

    Google Scholar 

  18. Huang, C.K., Chien, L.F., Oyang, Y.J.: Relevant term suggestion in interactive web search based on contextual information in query session logs. J. Am. Soc. Inf. Sci. Technol. 54(7), 638–649 (2003)

    Article  Google Scholar 

  19. Fonseca, B.M., Golgher, P., Pôssas, B., Ribeiro-Neto, B., Ziviani, N.: Concept-based interactive query expansion. In: Proc. ACM Int. Conf. Inf. Knowl. Manag., pp. 696–703 (2005)

    Google Scholar 

  20. Fan, J.W., Friedman, C.: Semantic classification of biomedical concepts using distributional similarity. J. Am. Med. Inform. Assoc. 14(4), 467–477 (2007)

    Article  Google Scholar 

  21. Bodenreider, O., McCray, A.T.: Exploring semantic groups through visual approaches. J. Biomed. Inform. 36(6), 414–432 (2003)

    Article  Google Scholar 

  22. Cohen, T., Schvaneveldt, R., Widdows, D.: Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections. J. Biomed. Inform. 43(2), 240–256 (2010)

    Article  Google Scholar 

  23. Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference on Research in Computational Linguistics (1997). arXiv preprint cmp-lg/9709008

    Google Scholar 

  24. Hersh, W.R., Buckley, C., Leone, T.J., Hickam D.H.: OHSUMED: an interactive retrieval evaluation and new large test collection for research. In: ACM SIGIR conference on Research and Development in Information Retrieval, pp. 192–201 (1994)

    Google Scholar 

  25. Liu, X., Croft, W.B.: Statistical language modeling for information retrieval. Annual Review of Information Science and Technology 39, 2–32 (2005)

    Google Scholar 

  26. Veerasamy, A., Belkin, N.J.: Evaluation of a tool for visualization of information retrieval results. In: SIGIR on Research and Development in Information Retrieval, pp. 85–92 (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Library and Information Science, Yonsei University, Seoul, Korea

    Erin Hea-Jin Kim & Min Song

  2. School of Information Sciences, University of Pittsburgh, Pittsburgh, USA

    Jung Sun Oh

Authors
  1. Erin Hea-Jin Kim
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  2. Jung Sun Oh
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  3. Min Song
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Corresponding author

Correspondence to Min Song .

Editor information

Editors and Affiliations

  1. Yonsei University, Seoul, Korea (Republic of)

    Robert B. Allen

  2. School of ITEE, University of Queensland, St. Lucia, Queensland, Australia

    Jane Hunter

  3. School of Library & Info Sci, Kent State University, KENT, Ohio, USA

    Marcia L. Zeng

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© 2015 Springer International Publishing Switzerland

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Kim, E.HJ., Oh, J.S., Song, M. (2015). Exploring Context-Sensitive Query Reformulation in a Biomedical Digital Library. In: Allen, R., Hunter, J., Zeng, M. (eds) Digital Libraries: Providing Quality Information. ICADL 2015. Lecture Notes in Computer Science(), vol 9469. Springer, Cham. https://doi.org/10.1007/978-3-319-27974-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-27974-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27973-2

  • Online ISBN: 978-3-319-27974-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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