Finding information on the World Wide Web: A specialty meta–search engine for the academic community

Yaffa Aharoni, Ariel Frank, Snunith Shoham

Abstract



The Web is continuing to grow rapidly and search engine technologies are evolving fast. Despite these developments, some problems still remain, mainly, difficulties in finding relevant, dependable information. This problem is exacerbated in the case of the academic community, which requires reliable scientific materials in various specialized research areas.

We propose that a solution for the academic community might be a meta–search engine which would allow search queries to be sent to several specialty search engines that are most relevant for the information needs of the academic community. The basic premise is that since the material indexed in the repositories of specialty search engines is usually controlled, it is more reliable and of better quality.

A database selection algorithm for a specialty meta–search engine was developed, taking into consideration search patterns of the academic community, features of specialty search engines and the dynamic nature of the Web.

This algorithm was implemented in a prototype of a specialty meta–search engine for the medical community called AcadeME. AcadeME’s performance was compared to that of a general search engine — represented by Google, a highly regarded and widely used search engine — and to that of a single specialty search engine — represented by the medical Queryserver. From the comparison to Google it was found that AcadeME contributed to the quality of the results from the point of view of the academic user. From the comparison to the medical Queryserver it was found that AcadeMe contributed to relevancy and to the variety of the results as well.


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DOI: http://dx.doi.org/10.5210/fm.v10i12.1297



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