Theory identity: A machine-learning approach

Kai R. Larsen, Dirk Hovorka, Jevin West, James Birt, James R. Pfaff, Trevor W. Chambers, Zebula R. Sampedro, Nick Zager, Bruce Vanstone

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014
PublisherIEEE Computer Society
Pages4639-4648
Number of pages10
ISBN (Print)9781479925049
DOIs
Publication statusPublished - 2014
Event47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, Waikoloa, HI, United States
Duration: 6 Jan 20149 Jan 2014

Conference

Conference47th Hawaii International Conference on System Sciences, HICSS 2014
CountryUnited States
CityWaikoloa, HI
Period6/01/149/01/14

Fingerprint

Taxonomies
Ecosystems
Ontology
Learning systems
Identification (control systems)

Cite this

Larsen, K. R., Hovorka, D., West, J., Birt, J., Pfaff, J. R., Chambers, T. W., ... 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). [6759171] IEEE Computer Society. https://doi.org/10.1109/HICSS.2014.564
Larsen, Kai R. ; Hovorka, Dirk ; West, Jevin ; Birt, James ; Pfaff, James R. ; Chambers, Trevor W. ; Sampedro, Zebula R. ; Zager, Nick ; Vanstone, Bruce. / Theory identity : A machine-learning approach. Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society, 2014. pp. 4639-4648
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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., 6759171, IEEE Computer Society, pp. 4639-4648, 47th Hawaii International Conference on System Sciences, HICSS 2014, Waikoloa, HI, United States, 6/01/14. https://doi.org/10.1109/HICSS.2014.564

Theory identity : A machine-learning approach. / Larsen, Kai R.; Hovorka, Dirk; West, Jevin; Birt, James; Pfaff, James R.; Chambers, Trevor W.; Sampedro, Zebula R.; Zager, Nick; Vanstone, Bruce.

Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society, 2014. p. 4639-4648 6759171.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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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. IEEE Computer Society. 2014. p. 4639-4648. 6759171 https://doi.org/10.1109/HICSS.2014.564