Theory identity: A machine-learning approach (in press)47th Hawaii Conference of System Sciences (2014)
AbstractTheory identity is a fundamental problem for researchers seeking to determine theory quality, create theory ontologies and taxonomies, or perform focused theory-specific reviews and meta-analyses. We demonstrate a novel machine-learning approach to theory identification based on citation data and article features. The multi-disciplinary ecosystem of articles which cite a theory’s originating paper is created and refined into the network of papers predicted to contribute to, and thus identify, a specific theory. We provide a ‘proof-of-concept’ for a highly-cited theory. Implications for crossdisciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.
- theory identity,
- theory ontologies
Publication DateJanuary 6, 2014
Citation InformationKai R Stirling, Dirk Hovorka, Jevin West, James Birt, et al.. "Theory identity: A machine-learning approach (in press)" 47th Hawaii Conference of System Sciences (2014)
Available at: http://works.bepress.com/james_birt/11/