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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2012 Vol.2(4): 453-457 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.166

MMSE: Design & Implementation of a Medical Meta Search Engine

Ali Rezaeian Joojadeh and Hamid Hassanpour

Abstract—We present a new next generation domain of meta search engine. A Medical Meta Search Engine (MMSE) was designed in this research for the users with no medical expertise. It is enhanced with the domain knowledge obtained from Unified Medical Language System (UMLS) to increase the effectiveness of the searches. The power of the system is based on the ability to understand the semantics of web pages and the user queries. This MMSE transforms a keyword search into a conceptual search. This medical meta search engine aims to generate maximum output with semantic value using minimum input from the user. Since this system is designed to help people seeking information about health on the web, our target users are not medical specialists who can effectively use the special jargon of medicine and access medical databases. Medical experts have the advantage of shrinking the answer set by expressing several terms using medical terminology. Medical meta search engine provides the same advantage to its users through the automated use of the medical domain knowledge in the background. The results of our experiments indicate that, expanding the queries with domain knowledge and increase dramatically the relevance of an answer set and the number of retrieved web pages that are relevant to the user request.

Index Terms—Medical information retrieval, UMLS, Medical, Meta search engine, Conceptual search.

Ali Rezaeian Joojadeh is with the Sama technical and vocatinal training college, Islamic Azad University, Sari Branch, Sari, Iran (e-mail:Alirezaeian_j@yahoo.com).


Cite: Ali Rezaeian Joojadeh and Hamid Hassanpour, "MMSE: Design & Implementation of a Medical Meta Search Engine," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 453-457, 2012.

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