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Theory identity: A machine-learning approach

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Theory identity: A machine-learning approach. / Larsen, Kai R.; Hovorka, Dirk; West, Jevin et al.
Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. United States: IEEE Computer Society Press, 2014. p. 4639-4648.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

HarvardHarvard

Larsen, KR, Hovorka, D, West, J, Birt, J, Pfaff, JR, Chambers, TW, Sampedro, ZR, Zager, N & Vanstone, B 2014, Theory identity: A machine-learning approach. in Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society Press, United States, pp. 4639-4648. https://doi.org/10.1109/HICSS.2014.564

APA

Larsen, K. R., Hovorka, D., West, J., Birt, J., Pfaff, J. R., Chambers, T. W., Sampedro, Z. R., Zager, N., & Vanstone, B. (2014). Theory identity: A machine-learning approach. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014 (pp. 4639-4648). IEEE Computer Society Press. https://doi.org/10.1109/HICSS.2014.564

CBE

Larsen KR, Hovorka D, West J, Birt J, Pfaff JR, Chambers TW, Sampedro ZR, Zager N, Vanstone B. 2014. Theory identity: A machine-learning approach. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. United States: IEEE Computer Society Press. pp. 4639-4648. https://doi.org/10.1109/HICSS.2014.564

MLA

Larsen, Kai R. et al. "Theory identity: A machine-learning approach". Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. United States: IEEE Computer Society Press. 2014, 4639-4648. https://doi.org/10.1109/HICSS.2014.564

VancouverVancouver

Larsen KR, Hovorka D, West J, Birt J, Pfaff JR, Chambers TW et al. Theory identity: A machine-learning approach. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. United States: IEEE Computer Society Press. 2014. p. 4639-4648 doi: 10.1109/HICSS.2014.564

Author

Larsen, Kai R. ; Hovorka, Dirk ; West, Jevin et al. / Theory identity: A machine-learning approach. Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. United States : IEEE Computer Society Press, 2014. pp. 4639-4648

RIS

TY - GEN

T1 - Theory identity: A machine-learning approach

AU - Larsen, Kai R.

AU - Hovorka, Dirk

AU - West, Jevin

AU - Birt, James

AU - Pfaff, James R.

AU - Chambers, Trevor W.

AU - Sampedro, Zebula R.

AU - Zager, Nick

AU - Vanstone, Bruce

N1 - 47th Hawaii International Conference on System Sciences, HICSS 2014 ; Conference date: 06-01-2014 Through 09-01-2014

PY - 2014

Y1 - 2014

N2 - 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 cross-disciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.

AB - 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 cross-disciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.

U2 - 10.1109/HICSS.2014.564

DO - 10.1109/HICSS.2014.564

M3 - Conference contribution

SN - 9781479925049

SP - 4639

EP - 4648

BT - Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014

PB - IEEE Computer Society Press

CY - United States

ER -