Abstract
Generic search engines have played a significant role in helping people locate their needed information on the web. However, they don’t perform as desired on domain-specific queries. In this paper, we focus on the domain of agriculture and develop a novel search engine specifically for agricultural disease prescription retrieval. In order to improve the performance of search for prescription documents, we exploit the domain-specific characteristics embedded in agricultural disease prescription, and propose a domainspecific query expansion approach as well as a BM25-based structural retrieval function. An intelligent search engine for agricultural disease prescription is then implemented based on the proposed retrieval model. User interfaces of the developed search engine are demonstrated.
Chapter PDF
Similar content being viewed by others
References
Apache Lucene (2013), http://lucene.apache.org/
Manning, C.D., Raghavan, P., SchĂĽtze, H.: Introduction toInformation Retrieval. Cambridge University Press (2008)
Liu, T.-Y.: Learning to Rank for Information Retrieval. Springer (2011)
Wilkinson, R.: Effective retrieval of structured documents. In: Proceedings of SIGIR, pp. 311–317 (1994)
Ogilvie, P., Callan, J.: Combining document representations for known-itemsearch. In: Proceedings of SIGIR, pp. 143–150 (2003)
Yi, X., Allan, J., Croft, W.B.: Matching resumes and jobs based on relevance models. In: Proceedings of SIGIR, pp. 809–810 (2007)
Zhao, L., Callan, J.: Effective and Efficient Structured Retrieval. In: Proceedingsof CIKM, pp. 1573–1576 (2009)
Huang, H.: Complex Adaptive Agriculture Vertical Search Model and its Implementation. Dissertation: University of Science and Technology of China (2010)
Zhou, P.: Research on key techniques of agricultural search engine. MS Thesis: Capital Normal University, China (2009)
AgriSou (2013), http://www.agrisou.com/
Sounong (2013), http://www.sounong.net/
Agr365 (2013), http://so.ag365.com/
AgNIC (2013), http://www.agnic.org/
Agriscape (2013), http://www.agriscape.com/
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Massachusetts (2011)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidategeneration. In: Proceedings of SIGMOD, pp. 1–12 (2000)
Robertson, S.E., Walker, S., Hancock-Beaulieu, M.: Okapi atTREC-7. In: Proceedings of TREC, pp. 199–210 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ni, W., Liu, M., Zeng, Q., Liu, T. (2014). An Intelligent Search Engine for Agricultural Disease Prescription. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture VII. CCTA 2013. IFIP Advances in Information and Communication Technology, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54341-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-54341-8_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54340-1
Online ISBN: 978-3-642-54341-8
eBook Packages: Computer ScienceComputer Science (R0)