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Developing a Taxonomy of Data Annotation Tools

Information on the Thesis

Type of Final Thesis: Bachelor
Supervisor: Simon Warsinsky
Research Group: Critical Information Infrastructures

Archive Number: 5.075
Status of Thesis: Open
Date of start: 2023-08-15

Further Information

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Problem: With the rise of supervised machine learning (ML) models also comes a surge in demand for high-quality annotated training data. To annotate data, a variety of different annotation tools exist that provide functions to enable and support effective data annotation. Functions of these tools range from simple ones like drawing polygons or setting tags via hotkey to enabling AI-based automated annotation or organizational functions like access control. Despite the plethora of available tools and the functions therein, we still frequently observe that individuals in need of annotated data often develop their own annotation tools to cater to their individual annotation needs.

Objective(s): The objective of this thesis is to synthesize existing annotation tool offerings and classify them into a taxonomy.

Method(s): Taxonomy Development following Nickerson et al. (2013)


• Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336-359.

• Rädsch, T., Eckhardt, S., Leiser, F., Pandl, K. D., Thiebes, S., & Sunyaev, A. (2021). What your radiologist might be missing: using machine learning to identify mislabeled instances of X-ray images.

• Di Mitri, D., Schneider, J., Klemke, R., Specht, M., & Drachsler, H. (2019, March). Read between the lines: An annotation tool for multimodal data for learning. In Proceedings of the 9th international conference on learning analytics & knowledge (pp. 51-60).

• Some links to annotation tools / providers: