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MEDQUAL: Improving Medical Web Search over Time with Dynamic Credibility Heuristics
MARK GINSBURG - University of Arizona - Department of Management Information Systems
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ABSTRACT
Performing a search on the World Wide Web (WWW) and traversing
the resulting links is an adventure in which one encounters both
credible and incredible web pages. Search engines, such as
Google, rely on macroscopic Web topology patterns and even
highly ranked 'authoritative' web sites may be a mixture of
informed and uninformed opinions. Without credibility heuristics
to guide the user in a maze of facts, assertions, and
inferences, the Web remains an ineffective knowledge delivery
platform. This report presents the design and implementation of
a modular extension to the popular Google search engine,
MEDQUAL, which provisions both URL and content-based heuristic
credibility rules to reorder raw Google rankings in the medical
domain. MEDQUAL, a software system written in Java, starts with
a bootstrap configuration file which loads in basic heuristics
in XML format. It then provides a subscription mechanism so
users can join birds of feather specialty groups, for example
Pediatrics, in order to load specialized heuristics as well. The
platform features a coordination mechanism whereby information
seekers can effectively become secondary authors, contributing
by consensus vote additional credibility heuristics. MEDQUAL
uses standard XML namespace conventions to divide opinion groups
so that competing groups can be supported simultaneously. The
net effect is a merger of basic and supplied heuristics so that
the system continues to adapt and improve itself over time to
changing web content, changing opinions, and new opinion groups.
The key goal of leveraging the intelligence of a large-scale and
diffuse WWW user community is met and we conclude by discussing
our plans to develop MEDQUAL further and evaluate it.
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