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Multimedia Tools and Applications

, Volume 71, Issue 2, pp 905–929 | Cite as

Expanding user’s query with tag-neighbors for effective medical information retrieval

  • Frederico Durao
  • Karunakar Bayyapu
  • Guandong Xu
  • Peter Dolog
  • Ricardo Lage
Article

Abstract

Medical information is a natural human demand. Existing search engines on the Web often are unable to handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. Under-specified queries often lead to undesirable search results that do not contain the information needed. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users’ original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the qualified tag neighbors to expand an entry query. The proposed approach is evaluated by using MedWorm medical article collection and results show considerable precision improvements over state-of-the-art approaches.

Keywords

Medical information retrieval Query expansion Tagging Health information system 

Notes

Acknowledgements

This work has been partially supported by FP7 ICT project M-Eco: Medical Ecosystem Personalized Event-Based Surveillance under grant number 247829. This journal is a extended version of previously published paper at the International Conference on Information Science and Applications (ICISA 2011).

References

  1. 1.
    Anderson TW, Anderson TW (1984) An introduction to multivariate statistical analysis, 2nd edn. Wiley-InterscienceGoogle Scholar
  2. 2.
    Andreou A (2005) Agissilaos andreou. Master thesis, Agissilaos andreouGoogle Scholar
  3. 3.
    Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-WesleyGoogle Scholar
  4. 4.
    Bianco CE (2009) Medical librarians’ uses and perceptions of social tagging. J Med Libr Assoc : JMLA 97(2):136–139CrossRefGoogle Scholar
  5. 5.
    Carpineto C, de Mori R, Romano G, Bigi B (2001) An information-theoretic approach to automatic query expansion. ACM Trans Inf Sys 19(1):1–27CrossRefGoogle Scholar
  6. 6.
    Clarke SJ, Willett P (1997) Estimating the recall performance of web search engines. Aslib Proc 49(7):184–189CrossRefGoogle Scholar
  7. 7.
    Díaz-Galiano M, Martín-Valdivia M, Ureña-López L (2009) Query expansion with a medical ontology to improve a multimodal information retrieval system. Comput Biol Med 39(4):396–403CrossRefGoogle Scholar
  8. 8.
    Diem LT, Chevallet J-P, Thuy DT (2007) Thesaurus-based query and document expansion in conceptual indexing with UMLS: Application in medical information retrieval. In: Research, Innovation and Vision for the Future, 2007 IEEE International Conference on concept, image, medical, retrieval, umls. pp 242–246. http://ieeexploreieee.org/xpls/abs_all.jsp?arnumber=4223080
  9. 9.
    Dozier C, Kondadadi R, Al-Kofahi K, Chaudhary M, Guo X (2007) Fast tagging of medical terms in legal text. In: Proceedings of the 11th international conference on artificial intelligence and law. ACM, ICAIL ’07, New York, USA, pp 253–260Google Scholar
  10. 10.
    Durao F, Dolog P (2010) Extending a hybrid tag-based recommender system with personalization. In: SAC ’10: proceedings of the 2010 ACM symposium on applied computing. ACM, New York, USA, pp 1723–1727Google Scholar
  11. 11.
    Durao F, Bayyapu K, Xu G, Dolog P, Lage R (2011) Using tag-neighbors for query expansion in medical information retrieval. Inf Sci and App (ICISA) 0:1–9Google Scholar
  12. 12.
    Efthimiadis EN (1993) A user-centred evaluation of ranking algorithms for interactive query expansion. In: SIGIR ’93: proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, USA, pp 146–159CrossRefGoogle Scholar
  13. 13.
    Fu WT, Kannampallil T, Kang R, He J (2010) Semantic imitation in social tagging. ACM Trans Comput-Hum Interact 17:12:1–12:37CrossRefGoogle Scholar
  14. 14.
    Gordon-Murnane L (2006) Social bookmarking, folksonomies, and web 2.0 tools. Searcher Mag Database Prof 14(6):26–28Google Scholar
  15. 15.
    Gruber T (2008) Collective knowledge systems: where the social web meets the semantic web. Web Semant 6:4–13CrossRefGoogle Scholar
  16. 16.
    Hatcher E, Gospodnetic O (2004) Lucene in action (in action series). Manning Publications Co., Greenwich, CT, USAGoogle Scholar
  17. 17.
    Hersh WR, Hickam DH (1998) How well do physicians use electronic information retrieval systems? a framework for investigation and systematic review. JAMA 280(15):1347–1352CrossRefGoogle Scholar
  18. 18.
    IWISPlatform (2012) https://sourceforge.net/projects/iwis/files/. Accessed 1 April 2012
  19. 19.
    Jain H, Thao C, Zhao H (2010) Enhancing electronic medical record retrieval through semantic query expansion. Information systems and e-business management, pp 1–17Google Scholar
  20. 20.
    Jang H, Song SK, Myaeng SH (2006) Semantic tagging for medical knowledge tracking. In: Engineering in medicine and biology society, 2006. EMBS ’06. 28th Annual international conference of the IEEEGoogle Scholar
  21. 21.
    Jansen BJ, Spink A, Bateman J, Saracevic T (1998) Real life information retrieval: a study of user queries on the web. SIGIR Forum 32(1):5–17CrossRefGoogle Scholar
  22. 22.
    Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446CrossRefGoogle Scholar
  23. 23.
    Jin S, Lin H, Su S (2009) Query expansion based on folksonomy tag co-occurrence analysis. In: GRC ’09 IEEE international conference on granular computing, 2009, pp 300–305Google Scholar
  24. 24.
    Johnson SB (1999) Semantic lexicon for medical language processing. J Am Med Inform Assoc 6(3):205–218CrossRefGoogle Scholar
  25. 25.
    Kelly D, Cushing A, Dostert M, Niu X, Gyllstrom K (2010) Effects of popularity and quality on the usage of query suggestions during information search. In: Proceedings of the 28th international conference on human factors in computing systems. ACM, New York, USA, CHI ’10, pp 45–54Google Scholar
  26. 26.
    Liu Z, Chu WW (2005) Knowledge-based query expansion to support scenario-specific retrieval of medical free text. In: SAC ’05: proceedings of the 2005 ACM symposium on applied computing. ACM, New York, USA, pp 1076–1083CrossRefGoogle Scholar
  27. 27.
    Lu Z, Kim W, Wilbur W (2009) Evaluation of query expansion using mesh in pubmed. Inf Retr 12:69–80CrossRefGoogle Scholar
  28. 28.
    Luo G, Tang C, Yang H, Wei X (2008) Medsearch: a specialized search engine for medical information retrieval. In: Proceeding of the 17th ACM conference on information and knowledge management. ACM, New York, USA, CIKM ’08, pp 143–152Google Scholar
  29. 29.
    Ma H, Yang H, King I, Lyu MR (2008) Learning latent semantic relations from clickthrough data for query suggestion. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, New York, USA, CIKM ’08, pp 709–718Google Scholar
  30. 30.
    Matos S, Arrais J, Maia-Rodrigues J, Oliveira J (2010) Concept-based query expansion for retrieving gene related publications from medline. BMC Bioinformatics 11(1):212CrossRefGoogle Scholar
  31. 31.
    MedWorm (2012) http://www.medworm.com. Accessed 1 April 2012
  32. 32.
    Mei Q, Zhou D, Church K (2008) Query suggestion using hitting time. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, New York, USA, CIKM ’08, pp 469–478Google Scholar
  33. 33.
    MeSH (2012) http://www.nlm.nih.gov/mesh. Accessed 1 April 2012
  34. 34.
    Milicevic A, Nanopoulos A, Ivanovic M (2010) Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 33(3):187–209CrossRefGoogle Scholar
  35. 35.
    Orange (2012) http://www.orange.com. Accessed 1 April 2012
  36. 36.
    PubMed (2012) http://www.medworm.com. Accessed 1 April 2012
  37. 37.
    Ravid G, Bar-Ilan J, Baruchson-Arbib S, Rafaeli S (2007) Popularity and findability through log analysis of search terms and queries: the case of a multilingual public service website. J Inf Sci 33(5):567–583CrossRefGoogle Scholar
  38. 38.
    Ruch P, Wagner J, Bouillon P, Baud RH, Rassinoux AM, Scherrer JR (1999) Medtag: tag-like semantics for medical document indexing. J Am Med Inform Assoc 6(3):205–218CrossRefGoogle Scholar
  39. 39.
    Smith G (2007) Tagging: people-powered metadata for the social web (voices that matter). New Riders PressGoogle Scholar
  40. 40.
    Strohmaier M (2008) Purpose tagging: capturing user intent to assist goal-oriented social search. In: Proceeding of the 2008 ACM workshop on search in social media. ACM, New York, USA, SSM ’08, pp 35–42Google Scholar
  41. 41.
    TREC (2012) http://trec.nist.gov. Accessed 1 April 2012
  42. 42.
  43. 43.
    West J (2007) Subject headings 2.0: folksonomies and tags. LMC 25(7):58–59Google Scholar
  44. 44.
    Yuan MJ, Orshalick J, Heute T (2009) Seam framework: experience the evolution of java EE, 2nd edn. Prentice Hall PTR, Upper Saddle River, NJ, USAGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Frederico Durao
    • 1
  • Karunakar Bayyapu
    • 2
  • Guandong Xu
    • 3
  • Peter Dolog
    • 1
  • Ricardo Lage
    • 1
  1. 1.IWIS — Intelligent Web and Information Systems, Computer Science DepartmentAalborg UniversityAalborg-EastDenmark
  2. 2.CBS — Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of DenmarkKongens LyngbyDenmark
  3. 3.Center for Applied InformaticsVictoria UniversityVictoriaAustralia

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