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 language | English (US) |
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Pages | 53-60 |
Number of pages | 8 |
State | Published - 2008 |
Externally published | Yes |
Event | 3rd International Symposium on Semantic Mining in Biomedicine, SMBM 2008 - Turku, Finland Duration: Sep 1 2008 → Sep 3 2008 |
Other
Other | 3rd International Symposium on Semantic Mining in Biomedicine, SMBM 2008 |
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Country/Territory | Finland |
City | Turku |
Period | 09/1/08 → 09/3/08 |
ASJC Scopus subject areas
- Computer Science Applications
- Biomedical Engineering