Physical Sciences and Mathematics Commons^™

Theory Identity: A Machine-Learning Approach, Kai Larsen, Dirk Hovorka, Jevin West, James Birt, James Pfaff, Trevor Chambers, Zebula Sampedro, Nick Zager, Bruce Vanstone

Bruce Vanstone

Theory 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.