title: Statistical algorithms for ontology-based annotation of scientific literature.
abstract: BACKGROUND
  Ontologies encode relationships within a domain in robust data structures that can be used to annotate data objects, including scientific papers, in ways that ease tasks such as search and meta-analysis. However, the annotation process requires significant time and effort when performed by humans. Text mining algorithms can facilitate this process, but they render an analysis mainly based upon keyword, synonym and semantic matching. They do not leverage information embedded in an ontology's structure.
  
  
  METHODS
  We present a probabilistic framework that facilitates the automatic annotation of literature by indirectly modeling the restrictions among the different classes in the ontology. Our research focuses on annotating human functional neuroimaging literature within the Cognitive Paradigm Ontology (CogPO). We use an approach that combines the stochastic simplicity of naïve Bayes with the formal transparency of decision trees. Our data structure is easily modifiable to reflect changing domain knowledge.
  
  
  RESULTS
  We compare our results across naïve Bayes, Bayesian Decision Trees, and Constrained Decision Tree classifiers that keep a human expert in the loop, in terms of the quality measure of the F1-mirco score.
  
  
  CONCLUSIONS
  Unlike traditional text mining algorithms, our framework can model the knowledge encoded by the dependencies in an ontology, albeit indirectly. We successfully exploit the fact that CogPO has explicitly stated restrictions, and implicit dependencies in the form of patterns in the expert curated annotations.
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: Chayan
authors.initials: C
authors.lastname: Chakrabarti
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Jones
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: George F
authors.initials: GF
authors.lastname: Luger
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: Jiawei F
authors.initials: JF
authors.lastname: Xu
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA ; Mind Research Network, Albuquerque, New Mexico, USA.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Turner
authors.affiliation: Department of Physics, Florida International University, Miami, Florida, USA.
authors.forename: Angela R
authors.initials: AR
authors.lastname: Laird
authors.affiliation: Mind Research Network, Albuquerque, New Mexico, USA ; Department of Psychology and the Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Turner
references.citation: Neuroinformatics. 2005;3(1):65-78
references.pmid: 15897617
references.citation: Neuroinformatics. 2012 Jan;10(1):57-66
references.pmid: 21643732
references.citation: Neuroinformatics. 2008 Sep;6(3):149-60
references.pmid: 18946742
references.citation: Front Neurosci. 2013 Dec 16;7:240
references.pmid: 24409112
references.citation: Hum Brain Mapp. 2005 May;25(1):185-98
references.pmid: 15846810
references.citation: BMC Bioinformatics. 2009 Feb 05;10 Suppl 2:S1
references.pmid: 19208184