TY - CHAP

T1 - The Augmented Theorist - Toward Automated Knowledge Extraction from Conceptual Models

AU - Scharfenberger, Jonas

AU - Funk, Burkhardt

AU - Mueller, Benjamin

N1 - Track: Advances in Theories, Methods and Philosophy , Beitrag 6

PY - 2021

Y1 - 2021

N2 - Like other disciplines, Information Systems is experiencing a growing volume of scholarly publications. This development exacerbates the threat of conceptual fragmentation. Previously, solutions based on repositories and databases were suggested to combat this issue, but the effort needed to build and maintain these solutions has impeded their widespread adoption. In response, the literature is exploring machine- learning-based approaches. We join this exploration proposing a computer vision approach to detecting conceptual models and extracting their constituents. The developed tool can serve as a foundation for automating the population of scientific databases describing theoretical models. We evaluate our deep learning approach against a sample of papers containing graphical theoretical models, and show that 81.5% of all constructs, items, and path coefficients can be correctly classified. This has the potential to significantly reduce manual efforts to populate scientific databases and can be an important step towards the augmentation of the work of theorists.

AB - Like other disciplines, Information Systems is experiencing a growing volume of scholarly publications. This development exacerbates the threat of conceptual fragmentation. Previously, solutions based on repositories and databases were suggested to combat this issue, but the effort needed to build and maintain these solutions has impeded their widespread adoption. In response, the literature is exploring machine- learning-based approaches. We join this exploration proposing a computer vision approach to detecting conceptual models and extracting their constituents. The developed tool can serve as a foundation for automating the population of scientific databases describing theoretical models. We evaluate our deep learning approach against a sample of papers containing graphical theoretical models, and show that 81.5% of all constructs, items, and path coefficients can be correctly classified. This has the potential to significantly reduce manual efforts to populate scientific databases and can be an important step towards the augmentation of the work of theorists.

KW - Informatics

UR - https://www.mendeley.com/catalogue/60862790-dc83-3144-9167-f569fda08a14/

M3 - Article in conference proceedings

SN - 978-1-7336325-9-1

BT - ICIS 2021 Proceedings

PB - Association for Information Systems

T2 - International Conference on Information Systems - ICIS 2021

Y2 - 12 December 2021 through 15 December 2021

ER -