AMMO: Automated Method For Mapping Ontologies
Publication: Context Specific Ontology Integration: A Bayesian Approach, Oral Presentation, Proceeding of AMIA Joint Summits on Translational Science, 2012
Publication: Hyper-Experiments: Bayesian Inference and Annotation For GEO, Podium Talk, Proceeding of AMIA Joint Summits on Translational Science, 2012
Kshitij Marwah (MIT CSAIL/Stanford University) , Dustin Katzin, Amin Zollanvari, Natalya F. Noy, Marco Ramoni and Gil Alterovitz
We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links. We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments.
April 2012, Context-Specific Ontology Integration, Cambridge Semantic Web Gathering, W3C MIT CSAIL