Theory identity: A machine-learning approachHawaii International Conference on System Sciences (HICSS)
Date of this Version1-6-2014
Document TypeConference Proceeding
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.
Citation InformationKai R. Larsen, Dirk Hovorka, Jevin West, James Birt, et al.. "Theory identity: A machine-learning approach" Hawaii International Conference on System Sciences (HICSS) Vol. 47 (2014) p. 4639 - 4648 ISSN: 1530-1605
Available at: http://works.bepress.com/james_birt/18/