The Augmented Theorist - Toward Automated Knowledge Extraction from Conceptual Models

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

Authors

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.
Original languageEnglish
Title of host publicationICIS 2021 Proceedings : Building sustainability and resilience with IS: A call for action
PublisherThe Association for Information Systems (AIS)
Publication date2021
Article number1975
ISBN (print)978-1-7336325-9-1
ISBN (electronic)978-1-7336325-9-1
Publication statusPublished - 2021
Event42nd International Conference on Information Systems - ICIS 2021 TREOs: Building Sustainability and Resilience with IS: A Call for Action - digital, Austin, United States
Duration: 12.12.202115.12.2021
Conference number: 42
https://icis2021.aisconferences.org/

Bibliographical note

Publisher Copyright:
© 2021 42nd International Conference on Information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

    Research areas

  • Deep Learning, Knowledge Extraction, Object Detection, Structural Equation Models, Theorizing

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