@article {832,
	title = {Query Expansion with Enhanced-BM25 Approach for Improving the Search Query Performance on Clustered Biomedical Literature Retrieval {\textendash}},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {04/2018},
	pages = {86-98},
	type = {Research},
	chapter = {85},
	abstract = {<p>The aim of this paper is to improve the search query performance of the biomedical literature by expanding the queries with most significant terms. Methods: In this article, an enhanced BM25 mathematical approach is proposed to retrieve the most query relevant literature from clustered Biomedical literature bank with query expansion from MeSH. The clustered biomedical topics are analyzed with different pre-processing methods and term-weighting functions and found the best values for the tuning parameters K1, b, K3 and the right combination of pre-processing and term weighting functions for improving the query performance in terms of Average Precision, Mean Average Precision and R-precision. Novelty/Improvements: In this approach, the existing best match retrieval technique is normalized with the calibrate constants K1 = 1.3, b = 0.75, K3 = 1.2 and the significant terms are identified with comparison of MeSH for query expansion. The retrieval performance is improved in terms of Mean Average Precision and R-precision</p>
},
	url = {http://dline.info/fpaper/jdim/v16i2/jdimv16i2_4.pdf},
	author = {ThayyabaKhatoon MD and A. Govardhan}
}
