Abstract
This paper investigates how retrieval using knowledge bases can be effectively translated to the consumer health search (CHS) domain. We posit that using knowledge bases for query reformulation may help to overcome some of the challenges in CHS. However, translating and implementing such approaches is nontrivial in CHS as it involves many design choices. We empirically evaluated the impact these different choices had on retrieval effectiveness. A state-of-the-art knowledge-base retrieval model—the Entity Query Feature Expansion model—was used to evaluate the following design choices: which knowledge base to use (specialised vs. generic), how to construct the knowledge base, how to extract entities from queries and map them to entities in the knowledge base, what part of the knowledge base to use for query expansion, and if to augment the KB search process with relevance feedback. While knowledge base retrieval has been proposed as a solution for CHS, this paper delves into the finer details of doing this effectively, highlighting both pitfalls and payoffs. It aims to provide some lessons to others in advancing the state-of-the-art in CHS.
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Notes
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A Wikipedia Infobox is used to summarise important aspects of an entity and its relation with other articles.
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Only complete string matches were considered.
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ECNU-2 had the highest effectiveness, but it used Google query suggestion service to gain expansions.
References
Bendersky, M., Metzler, D., Croft, W.: Effective query formulation with multiple information sources. In: WSDM 2012, pp. 443–452 (2012)
Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: SIGIR 2014, pp. 365–374 (2014)
Díaz-Galiano, M., Martín-Valdivia, M., Ureña-López, L.: Query expansion with a medical ontology to improve a multimodal information retrieval system. JCBM 39(4), 396–403 (2009)
Jimmy, Zuccon, G., Koopman, B.: Boosting titles does not generally improve retrieval effectiveness. In: ADCS 2016, pp. 25–32 (2016)
Keselman, A., Tse, T., Crowell, J., Browne, A., Ngo, L., Zeng, Q.: Relating consumer knowledge of health terms and health concepts. In: AMIA 2006 (2006)
Limsopatham, N., Macdonald, C., Ounis, I.: Inferring conceptual relationships to improve medical records search. In: OAIR 2013, pp. 1–8 (2013)
Palotti, J., Goeuriot, L., Zuccon, G., Hanbury, A.: Ranking health web pages with relevance and understandability. In: SIGIR 2016, pp. 965–968 (2016)
Plovnick, R., Zeng, Q.: Reformulation of consumer health queries with professional terminology: a pilot study. JMIR, 6(3) (2004)
Silva, R., Lopes, C.: The effectiveness of query expansion when searching for health related content: Infolab at CLEF eHealth 2016. In: CLEF 2016 (2016)
Soldaini, L., Edman, W., Goharian, N.: Team GU-IRLAB at CLEF eHealth 2016: Task 3. In: CLEF (Working Notes), pp. 143–146 (2016)
Soldaini, L., Goharian, N.: QuickUMLS: a fast, unsupervised approach for medical concept extraction. In: SIGIR MedIR 2016, Pisa, Italy (2016)
Soldaini, L., Goharian, N.: Learning to rank for consumer health search: a semantic approach. In: Jose, J.M., Hauff, C., Altıngovde, I.S., Song, D., Albakour, D., Watt, S., Tait, J. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 640–646. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_60
Toms, E., Latter, C.: How consumers search for health information. HIJ 13(3), 223–235 (2007)
Zeng, Q., Kogan, S., Ash, N., Greenes, R., Boxwala, A.: Characteristics of consumer terminology for health information retrieval. JMIM 41(4), 289–298 (2002)
Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim, E., Ngo, L., Dibble, E.: Assisting consumer health information retrieval with query recommendations. JAMIA 13(1), 80–90 (2006)
Zhang, Y.: Searching for specific health-related information in MedlinePlus: behavioral patterns and user experience. JAIST 65(1), 53–68 (2014)
Zuccon, G., Koopman, B., Palotti, J.: Diagnose this if you can: on the effectiveness of search engines in finding medical self-diagnosis information. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 562–567. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_62
Zuccon, G., Palotti, J., Goeuriot, L., Kelly, L., Lupu, M., Pecina, P., Mueller, H., Budaher, J., Deacon, A.: The IR task at the CLEF eHealth evaluation lab 2016: user-centred health information retrieval. In: CLEF 2016 (2016)
Acknowledgements
Jimmy is sponsored by the Indonesia Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan/LPDP). Guido Zuccon is the recipient of an Australian Research Council DECRA Research Fellowship (DE180101579).
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Jimmy, Zuccon, G., Koopman, B. (2018). Choices in Knowledge-Base Retrieval for Consumer Health Search. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_6
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