The impact of directionality in predications on text mining

Gondy Leroy, Marcelo Fiszman, Thomas C. Rindflesch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS
DOIs
StatePublished - 2008
Event41st Annual Hawaii International Conference on System Sciences 2008, HICSS - Big Island, HI, United States
Duration: Jan 7 2008Jan 10 2008

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Other

Other41st Annual Hawaii International Conference on System Sciences 2008, HICSS
Country/TerritoryUnited States
CityBig Island, HI
Period1/7/081/10/08

ASJC Scopus subject areas

  • Engineering(all)

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