TY - CHAP

T1 - Parsing Causal Models – An Instance Segmentation Approach

AU - Scharfenberger, Jonas

AU - Funk, Burkhardt

N1 - Conference code: 35

PY - 2023/6/8

Y1 - 2023/6/8

N2 - The steadily growing number of publications in the field of information systems as well as the confusion arising from the naming of theoretical concepts, complicate the process of literature reviewing. While several knowledge repositories and databases are developed to combat this issue, a considerable amount of manual effort to populate the databases is required. The information these tools seek to present is often compactly summarized in causal models with a graph-like structure (e.g., structural equation models). Our work aims to develop a graph parsing method that reduces the amount of manual effort required and thus builds a foundation towards an augmentation of knowledge extraction from causal models. We contribute to the ongoing efforts in developing graph parsing tools by proposing a novel instance segmentation-based approach that leverages a new method to generate annotated synthetic graph images. Our solution is evaluated on a dataset of 166 images of structural equation models and outperforms existing graph parsing approaches in this use case.

AB - The steadily growing number of publications in the field of information systems as well as the confusion arising from the naming of theoretical concepts, complicate the process of literature reviewing. While several knowledge repositories and databases are developed to combat this issue, a considerable amount of manual effort to populate the databases is required. The information these tools seek to present is often compactly summarized in causal models with a graph-like structure (e.g., structural equation models). Our work aims to develop a graph parsing method that reduces the amount of manual effort required and thus builds a foundation towards an augmentation of knowledge extraction from causal models. We contribute to the ongoing efforts in developing graph parsing tools by proposing a novel instance segmentation-based approach that leverages a new method to generate annotated synthetic graph images. Our solution is evaluated on a dataset of 166 images of structural equation models and outperforms existing graph parsing approaches in this use case.

KW - Graph parsing

KW - Instance segmentation

KW - Structural equation models

KW - Synthetic data

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85163957389&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/a618afd7-de24-3150-ba45-89440ce6ca2f/

U2 - 10.1007/978-3-031-34674-3_6

DO - 10.1007/978-3-031-34674-3_6

M3 - Article in conference proceedings

AN - SCOPUS:85163957389

SN - 978-3-031-34673-6

T3 - Lecture Notes in Business Information Processing

SP - 43

EP - 51

BT - Intelligent Information Systems - CAiSE Forum 2023, Proceedings

A2 - Cabanillas, Cristina

A2 - Pérez, Francisca

PB - Springer Science and Business Media Deutschland

T2 - 35th International Conference on Advanced Information Systems Engineering - CAiSE 2023

Y2 - 12 June 2023 through 16 June 2023

ER -