Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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. ed. / N. Gronau; M. Heine; H. Krasnova; K. Pousttchi. Vol. 1 Berlin: GITO mbH Verlag, 2020. p. 115-120 8 (WI 2020; Vol. 1).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Genz, T & Funk, B 2020, Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers. in N Gronau, M Heine, H Krasnova & K Pousttchi (eds), Entwicklungen, Chancen und Herausforderungen der Digitalisierung: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020. vol. 1, 8, WI 2020, vol. 1, GITO mbH Verlag, Berlin, pp. 115-120, Internationale Tagung Wirtschaftsinformatik - WI 2020, Potsdam, Germany, 09.03.20. https://doi.org/10.30844/wi_2020_a8-genz, https://doi.org/10.30844/wi_2020_zt

APA

Genz, T., & Funk, B. (2020). Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers. In N. Gronau, M. Heine, H. Krasnova, & K. Pousttchi (Eds.), Entwicklungen, Chancen und Herausforderungen der Digitalisierung: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020 (Vol. 1, pp. 115-120). Article 8 (WI 2020; Vol. 1). GITO mbH Verlag. https://doi.org/10.30844/wi_2020_a8-genz, https://doi.org/10.30844/wi_2020_zt

Vancouver

Genz T, Funk B. Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers. In Gronau N, Heine M, Krasnova H, Pousttchi K, editors, Entwicklungen, Chancen und Herausforderungen der Digitalisierung: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020. Vol. 1. Berlin: GITO mbH Verlag. 2020. p. 115-120. 8. (WI 2020). doi: 10.30844/wi_2020_a8-genz, 10.30844/wi_2020_zt

Bibtex

@inbook{88588ff2fde543e4b410c1dfe76f60b2,
title = "Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers",
abstract = "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.",
keywords = "Business informatics, Structural equation models, deep neural networks, information extraction, literature review, Structural equation models, deep neural networks, information extraction, literature review",
author = "Tobias Genz and Burkhardt Funk",
note = "Bd. 1: Zentrale Tracks. Motto “Changing Landscapes{"} Publisher Copyright: {\textcopyright} Proceedings of the 15th International Conference on Business Information Systems 2020 {"}Developments, Opportunities and Challenges of Digitization{"}, WIRTSCHAFTSINFORMATIK 2020.; Internationale Tagung Wirtschaftsinformatik - WI 2020 ; Conference date: 09-03-2020 Through 11-03-2020",
year = "2020",
month = mar,
day = "9",
doi = "10.30844/wi_2020_a8-genz",
language = "English",
volume = "1",
series = "WI 2020",
publisher = "GITO mbH Verlag",
pages = "115--120",
editor = "N. Gronau and Heine, {M. } and H. Krasnova and K. Pousttchi",
booktitle = "Entwicklungen, Chancen und Herausforderungen der Digitalisierung",
address = "Germany",
url = "https://wi2020.de/de/start",

}

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

T2 - Internationale Tagung Wirtschaftsinformatik - WI 2020

Y2 - 9 March 2020 through 11 March 2020

ER -

DOI

Recently viewed

Researchers

  1. Kerstin Fedder

Publications

  1. Study on the effects of tool design and process parameters on the robustness of deep drawing
  2. Neural correlates of the enactment effect in the brain
  3. A Structure and Content Prompt-based Method for Knowledge Graph Question Answering over Scholarly Data
  4. Children's use of spatial skills in solving two map-reading tasks in real space.
  5. Outperformed by a Computer? - Comparing Human Decisions to Reinforcement Learning Agents, Assigning Lot Sizes in a Learning Factory
  6. Modeling Grounding Processes in Chat-based CSCL
  7. Foreign bias in institutional portfolio allocation
  8. Cross-case knowledge transfer in transformative research: enabling learning in and across sustainability-oriented labs through case reporting
  9. Masked Autoencoder Pretraining for Event Classification in Elite Soccer
  10. Comparison of EKF and TSO for Health Monitoring of a Textile-Based Heater Structure and its Control
  11. Determinants and Outcomes of Dual Distribution:
  12. The identification of up-And downstream industries using input-output tables and a firm-level application to minority shareholdings
  13. Sliding mode and model predictive control for inverse pendulum
  14. A Hermeneutic Interpretation of Concepts in a Cooperative Multicultural Working Project
  15. Multilayer neural networks
  16. Teachers’ temporary support and worked-out examples as elements of scaffolding in mathematical modeling
  17. Self-supervised Siamese Autoencoders
  18. Explaining Disagreement on Interest Rates in a Taylor-Rule Setting
  19. Audio-Hacks
  20. Geodesign as a boundary management process
  21. Octanol-Water Partition Coefficient Measurement by a Simple 1H NMR Method
  22. A New Approach for Optimal Solving Cyclic and Non-Cyclic Bus Drvier Rostering Problems
  23. Online-scheduling using past and real-time data
  24. Semiparametric one-step estimation of a sample selection model with endogenous covariates
  25. Negotiation complexity
  26. Design, Modeling and Control of an Over-actuated Hexacopter Tilt-Rotor
  27. Continued logarithm representation of real numbers
  28. Science-Related Outcomes