2014 47th Hawaii International Conference on System Sciences (2008)

Waikoloa, Big Island, Hawaii

Jan. 7, 2008 to Jan. 10, 2008

ISSN: 1530-1605

ISBN: 0-7695-3075-3

pp: 228

ABSTRACT

The number of publications in biomedicine is increasing enormously each year. To help researchers digest the information in these documents, text mining tools are being developed that present co-occurrence relations between concepts. Statistical measures are used to mine interesting subsets of relations. We demonstrate how directionality of these relations affects interestingness. Support and confidence, simple data mining statistics, are used as proxies for interestingness metrics. We first built a test bed of 126,404 directional relations extracted from biomedical abstracts, which we represent as graphs containing a central starting concept and 2 rings of associated relations. We manipulated directionality in four ways and randomly selected 100 starting concepts as a test sample for each graph type. Finally, we calculated the number of relations and their support and confidence. Variation in directionality significantly affected the number of relations as well as the support and confidence of the four graph types.

INDEX TERMS

CITATION

Marcelo Fiszman,
Thomas C. Rindflesch,
Gondy Leroy,
"The Impact of Directionality in Predications on Text Mining",

*2014 47th Hawaii International Conference on System Sciences*, vol. 00, no. , pp. 228, 2008, doi:10.1109/HICSS.2008.443