Journal of Medical Systems

, 41:34 | Cite as

Bat-Inspired Algorithm Based Query Expansion for Medical Web Information Retrieval

Systems-Level Quality Improvement
Part of the following topical collections:
  1. Health Information Systems & Technologies


With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients’ needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline.


Medical data management Web intelligence Query expansion Retrieval feedback Swarm intelligence Bat algorithm MEDLINE 


  1. 1.
    Alihodzic, A., and Tuba, M. Improved Bat Algorithm Applied to Multilevel Image Thresholding. The Scientific World Journal (2014)Google Scholar
  2. 2.
    Attardi, G., Atzori, L., Simi, M.: Index expansion for machine reading and question answering. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes (2012)Google Scholar
  3. 3.
    Bernardini, A., Carpineto, C., D’Amico, M.: Full-subtopic retrieval with keyphrase-based search results clustering. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 206–213. IEEE (2009)Google Scholar
  4. 4.
    Bindal, A.K., and Sanyal, S.: Query optimization in context of pseudo relevant documents. In: 3rd Italian Information Retrieval Workshop (2012)Google Scholar
  5. 5.
    de Boer, M., Schutte, K., Kraaij, W., Knowledge based query expansion in complex multimedia event detection. Multimedia Tools and Applications,1–19, 2015.Google Scholar
  6. 6.
    Cao, G., Nie, J.Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 243–250. ACM (2008)Google Scholar
  7. 7.
    Carpineto, C., and Romano, G. Concept Data Analysis: Theory and Applications. Wiley (2004)Google Scholar
  8. 8.
    Carpineto, C., and Romano, G., A survey of automatic query expansion in information retrieval. ACM Comput. Surveys 44(1):1–50, 2012.CrossRefGoogle Scholar
  9. 9.
    Chandrasekar, C., An optimized approach of modified bat algorithm to record deduplication. Int. J. Comput. Appl. 62(1), 2013.Google Scholar
  10. 10.
    Crestani, F., Application of spreading activation techniques in information retrieval. Artif. Intell. Rev. 11(6): 453–482, 1997.CrossRefGoogle Scholar
  11. 11.
    Curé, O.C., Maurer, H., Shah, N.H., Le Pendu, P., A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest. BMC Med. Inf. Decis. Making 15(Suppl 1):S8, 2015.CrossRefGoogle Scholar
  12. 12.
    Dao, T.K., Pan, T.S., Pan, J.S., Parallel bat algorithm for optimizing makespan in job shop scheduling problems. J. Intell. Manuf.,1–12, 2015.Google Scholar
  13. 13.
    Díaz-Galiano, M.C., Martín-Valdivia, M.T., Ureña-López, L., Query expansion with a medical ontology to improve a multimodal information retrieval system. Comput. Biol. Med. 39(4):396–403, 2009.CrossRefPubMedGoogle Scholar
  14. 14.
    Durao, F., Bayyapu, K., Xu, G., Dolog, P., Lage, R., Expanding user’s query with tag-neighbors for effective medical information retrieval. Multimed. Tools Appl. 71(2):905–929 , 2014.CrossRefGoogle Scholar
  15. 15.
    Gao, K., Zhang, Y., Zhang, D., Lin, S., Accurate off-line query expansion for large-scale mobile visual search. Signal Process. 93(8):2305–2315, 2013.CrossRefGoogle Scholar
  16. 16.
    Hersh, W., Buckley, C., Leone, T., Hickam, D. Ohsumed: An interactive retrieval evaluation and new large test collection for research. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 192–201. Springer (1994)Google Scholar
  17. 17.
    Jaddi, N.S., Abdullah, S., Hamdan, A.R., Optimization of neural network model using modified bat-inspired algorithm. Appl. Soft Comput. 37:71–86, 2015.CrossRefGoogle Scholar
  18. 18.
    Jain, H., Thao, C., Zhao, H., Enhancing electronic medical record retrieval through semantic query expansion. Inf. Syst. e-Business Manag. 10(2):165–181, 2012.CrossRefGoogle Scholar
  19. 19.
    Jalali, V., and Borujerdi, M.R.M., Information retrieval with concept-based pseudo-relevance feedback in medline. Knowledge Inf. Syst. 29(1):237–248, 2011.CrossRefGoogle Scholar
  20. 20.
    Jouglet, A., and Carlier, J., Dominance rules in combinatorial optimization problems. Eur. J. Oper. Res. 212(3):433–444 , 2011.CrossRefGoogle Scholar
  21. 21.
    Kennedy, J. Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011)Google Scholar
  22. 22.
    Kennedy, J., Kennedy, J.F., Eberhart, R.C., Shi, Y. Swarm Intelligence. Morgan Kaufmann (2001)Google Scholar
  23. 23.
    Khennak, I., and Drias, H. Bat algorithm for efficient query expansion: Application to medline. In: Proceedings of the 4th World Conference on Information Systems and Technologies, pp. 113–122. Springer (2016)Google Scholar
  24. 24.
    Komarasamy, G., and Wahi, A., An optimized k-means clustering technique using bat algorithm. Eur. J. Sci. Res. 84(2):26–273, 2012.Google Scholar
  25. 25.
    Lee, A., and Chau, M.: The impact of query suggestion in e-commerce websites. In: E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life 10th Workshop on E-Business, WEB 2011, pp. 248–254 (2011)Google Scholar
  26. 26.
    Lee, K.S., and Croft, W.B., A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback. Inf. Process. Manag. 49(4):792–806, 2013.CrossRefGoogle Scholar
  27. 27.
    Leturia, I., Gurrutxaga, A., Areta, N., Alegria, I., Ezeiza, A., Morphological query expansion and language-filtering words for improving basque web retrieval. Lang. Resour. Eval. 47(2):425–448, 2013.CrossRefGoogle Scholar
  28. 28.
    Lu, Z., Kim, W., Wilbur, W.J., Evaluation of query expansion using mesh in pubmed. Inf. Retriev. 12 (1):69–80, 2009.CrossRefGoogle Scholar
  29. 29.
    Melucci, M., A basis for information retrieval in context. ACM Transactions on Information Systems 26(3): 14:1–14:41, 2008.CrossRefGoogle Scholar
  30. 30.
    Miao, J., Huang, J.X., Ye, Z.: Proximity-based rocchio’s model for pseudo relevance. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 535–544. ACM (2012)Google Scholar
  31. 31.
    Pérez, J., Valdez, F., Castillo, O.: A new bat algorithm with fuzzy logic for dynamical parameter adaptation and its applicability to fuzzy control design. In: Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, pp. 65–79. Springer (2015)Google Scholar
  32. 32.
    Robertson, S., and Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc (2009)Google Scholar
  33. 33.
    Robertson, S.E., and Jones, K.S., Relevance weighting of search terms. J. Amer. Soc. Inf. Sci. 27(3):129–146, 1976.CrossRefGoogle Scholar
  34. 34.
    Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M., et al., Okapi at trec-3. NIST Spec. Publ. SP 109:109, 1995.Google Scholar
  35. 35.
    Rocchio, J.J., Relevance feedback in information retrieval. SMART Retriev. Syst. Exper. Autom. Doc. Process., 313–323, 1971.Google Scholar
  36. 36.
    Sahlgren, M.: An introduction to random indexing. In: Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering, TKE (2005)Google Scholar
  37. 37.
    Véronis, J., Hyperlex: Lexical cartography for information retrieval. Comput. Speech Lang. 18(3):223–252, 2004.CrossRefGoogle Scholar
  38. 38.
    Wong, S.K., Ziarko, W., Raghavan, V.V., Wong, P.C., On modeling of information retrieval concepts in vector spaces. ACM Trans. Data. Syst. 12(2):299–321, 1987.CrossRefGoogle Scholar
  39. 39.
    Wu, I.C., Chen, G.W., Hsu, J.L., Lin, C.Y., An entropy-based query expansion approach for learning researchers’ dynamic information needs. Knowledge-Based Syst. 52:133–146, 2013.CrossRefGoogle Scholar
  40. 40.
    Yang, N.C., and Le, M.D., Optimal design of passive power filters based on multi-objective bat algorithm and pareto front. Appl. Soft Comput. 35:257–266, 2015.CrossRefGoogle Scholar
  41. 41.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  42. 42.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms: Second Edition. Luniver Press (2010)Google Scholar
  43. 43.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization, pp. 65–74 (2010)Google Scholar
  44. 44.
    Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier (2014)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Laboratory for Research in Artificial IntelligenceComputer Science Department, USTHBAlgiersAlgeria

Personalised recommendations