Paper Info
Title
Using annotations from controlled vocabularies to find meaningful associations
Abstract
This paper presents the LSLink (or Life Science Link) methodology that provides users with a set of tools to explore the rich Web of interconnected and annotated objects in multiple repositories, and to identify meaningful associations. Consider a physical link between objects in two repositories, where each of the objects is annotated with controlled vocabulary (CV) terms from two ontologies. Using a set of LSLink instances generated from a background dataset of knowledge we identify associations between pairs of CV terms that are potentially significant and may lead to new knowledge. We develop an approach based on the logarithm of the odds (LOD) to determine a confidence and support in the associations between pairs of CV terms. Using a case study of Entrez Gene objects annotated with GO terms linked to PubMed objects annotated with MeSH terms, we describe a user validation and analysis task to explore potentially significant associations.
Year
Venue
Keywords
2007
DILS
controlled vocabulary,analysis task,significant association,annotated object,meaningful association,life science link,new knowledge,mesh term,pubmed object,lslink instance,cv term,entrez gene object,controlled vocabularies,lod
Field
DocType
Volume
Ontology (information science),Data mining,Information retrieval,Computer science,Controlled vocabulary,Entrez Gene,Logarithm,Odds,Database
Conference
4544
ISSN
Citations 
PageRank 
0302-9743
10
0.74
References 
Authors
20
6
Name
Order
Citations
PageRank
Woei-Jyh Lee114717.08
Louiqa Raschid21522417.56
Padmini Srinivasan31645132.49
Nigam Shah421220.11
Daniel Rubin520111.86
Natalya Fridman Noy65006381.51