Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Standard
Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers. / Genz, Tobias; Funk, Burkhardt.
Entwicklungen, Chancen und Herausforderungen der Digitalisierung: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020. Hrsg. / N. Gronau; M. Heine; H. Krasnova; K. Pousttchi. Band 1 Berlin : GITO mbH Verlag, 2020. S. 115-120 8 (WI 2020; Band 1).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers
AU - Genz, Tobias
AU - Funk, Burkhardt
N1 - Conference code: 15
PY - 2020/3/9
Y1 - 2020/3/9
N2 - Literature reviews are an essential but time-consuming part of every research endeavor and play an important role in the quality of the research findings. Traditional tools and literature databases only make use of the textual information and do not consider graphical representations like figures of structural equation models (SEMs). These models are often used in empirical studies to visualize theoretical models and key results. We design and implement an application for image recognition to simplify the search for relevant papers, by automatically recognizing SEM figures in scientific papers stored as PDF files. To classify whether a page in a paper contains an SEM figure we make use of convolutional neural networks and achieve an F1 score of 98,7% together with a recall of 100% for the SEM class. We further describe how we intend to automatically extract information from these SEM figures.
AB - Literature reviews are an essential but time-consuming part of every research endeavor and play an important role in the quality of the research findings. Traditional tools and literature databases only make use of the textual information and do not consider graphical representations like figures of structural equation models (SEMs). These models are often used in empirical studies to visualize theoretical models and key results. We design and implement an application for image recognition to simplify the search for relevant papers, by automatically recognizing SEM figures in scientific papers stored as PDF files. To classify whether a page in a paper contains an SEM figure we make use of convolutional neural networks and achieve an F1 score of 98,7% together with a recall of 100% for the SEM class. We further describe how we intend to automatically extract information from these SEM figures.
KW - Business informatics
KW - Structural equation models
KW - deep neural networks
KW - information extraction
KW - literature review
KW - Structural equation models
KW - deep neural networks
KW - information extraction
KW - literature review
UR - http://www.scopus.com/inward/record.url?scp=85101711718&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/0f33ecfe-9090-30a3-919f-3def5d3cd44e/
U2 - 10.30844/wi_2020_a8-genz
DO - 10.30844/wi_2020_a8-genz
M3 - Article in conference proceedings
VL - 1
T3 - WI 2020
SP - 115
EP - 120
BT - Entwicklungen, Chancen und Herausforderungen der Digitalisierung
A2 - Gronau, N.
A2 - Heine, M.
A2 - Krasnova, H.
A2 - Pousttchi, K.
PB - GITO mbH Verlag
CY - Berlin
Y2 - 9 March 2020 through 11 March 2020
ER -