Parsing Causal Models – An Instance Segmentation Approach

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


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.

Original languageEnglish
Title of host publicationIntelligent Information Systems - CAiSE Forum 2023, Proceedings
EditorsCristina Cabanillas, Francisca Pérez
Number of pages9
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date08.06.2023
ISBN (print)978-3-031-34673-6
ISBN (electronic)978-3-031-34674-3
Publication statusPublished - 08.06.2023
Event35th International Conference on Advanced Information Systems Engineering - CAiSE 2023: Cyber-Human Systems - SVIT at San Jorge University, Zaragoza, Spain
Duration: 12.06.202316.06.2023
Conference number: 35

Bibliographical note

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© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.