From terms to categories: Testing the significance of co-occurrences between ontological categories

Robert Hoehndorf*, Axel Cyrille Ngonga Ngomo, Michael Dannemann, Janet Kelso

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The co-occurrence of terms in a text corpus may indicate the presence of a relation between the referents of these terms. We expect co-occurrence-based methods to identify association relations that cannot be found using static patterns. We developed a new method to identify associations between ontological categories in text using the co-occurrence of terms that designate these categories. We use the taxonomic structure of the ontologies to cumulate the number of co-occurrences of terms designating categories. Based on these cumulated values, we designed a novel family of statistical tests to identify associated categories. These tests take both co-occurrence specificity and relevance into consideration. We applied our method to a 2.2 GB text corpus containing fulltext articles and used Gene Ontology's biological process ontology and the Celltype Ontology. The software and results can be found at http://bioonto.de/pmwiki.php/Main/ExtractingBiologicalRelations.

Original languageEnglish (US)
Title of host publication3rd International Symposium on Semantic Mining in Biomedicine, SMBM 2008 - Proceedings
Pages53-60
Number of pages8
StatePublished - 2008
Externally publishedYes
Event3rd International Symposium on Semantic Mining in Biomedicine, SMBM 2008 - Turku, Finland
Duration: Sep 1 2008Sep 3 2008

Other

Other3rd International Symposium on Semantic Mining in Biomedicine, SMBM 2008
CountryFinland
CityTurku
Period09/1/0809/3/08

ASJC Scopus subject areas

  • Computer Science Applications
  • Biomedical Engineering

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