Artificial Intelligence Lab, Department of MIS, University of Arizona / Medical professionals and researchers need information
from reputable sources to accomplish their work. Unfortunately,
the Web has a large number of documents that
are irrelevant to their work, even those documents that
purport to be â medically-related.â This paper describes
an architecture designed to integrate advanced searching
and indexing algorithms, an automatic thesaurus, or
â concept space,â and Kohonen-based Self-Organizing
Map (SOM) technologies to provide searchers with finegrained
results. Initial results indicate that these systems
provide complementary retrieval functionalities.
HelpfulMed not only allows users to search Web pages
and other online databases, but also allows them to
build searches through the use of an automatic thesaurus
and browse a graphical display of medical-related
topics. Evaluation results for each of the different components
are included. Our spidering algorithm outperformed
both breadth-first search and PageRank spiders
on a test collection of 100,000 Web pages. The automatically
generated thesaurus performed as well as both
MeSH and UMLSâ systems which require human mediation
for currency. Lastly, a variant of the Kohonen SOM
was comparable to MeSH terms in perceived cluster
precision and significantly better at perceived cluster
recall.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105202 |
| Date | 05 1900 |
| Creators | Chen, Hsinchun, Lally, Ann M., Zhu, Bin, Chau, Michael |
| Publisher | Wiley Periodicals, Inc |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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