Guiding theme D1: Multi-level models of information quality in online scenarios

Guiding theme D1 conducts research on criteria and novel evaluation methods of assessing heterogeneous document sources used for the automatic document summarization. The heterogeneity of documents in the Web, User generated content and collaborative generated content face new encompassing challenges for the evaluation of quality and trustworthiness of the documents. Existing information quality frameworks provide a basis for the development of a new model related to the scope of AIPHES in general and in particular multi document summarization. The intended use of the information resp. documents has to be considered defining the criteria. In addition the model has to be composed of different levels which have to be inspected each.

A PhD project in this area will therefore focus on creating these multi-level model and a comprehensive framework for the evaluation of heterogeneous document sources. The quality assessment framework will be developed in close collaboration with guiding theme D2: Manual and automatic Quality Assessment of Summaries from Heterogeneous Sources. The student will cooperate with the user esp. from online editorial teams. He or she will monitor, document and analyze the editorial processes in different companies and use the experiences of our cooperation partner Institut für Kommunikation und Medien (IKUM) at Hochschule Darmstadt.

Poster (in German)

Example thesis topics

  • Assesment of information quality including external knowledge resources
  • Adaptation of information quality metrics for different genres and application domains


Eppler, M. und Wittig, D. (2000). Conceptualizing Information Quality: A Review of Information Quality Frameworks from the Last Ten Years. In Proceedings of the 2000 Conference on Information Quality, Cambridge, Massachusetts, USA, 20 - 22 October 2000, pages 83–96.

Madnick, S. E., Wang, R. Y., Lee, Y. W., und Zhu, H. (2009). Overview and Framework for Data and Information Quality Research. ACM Journal of Data and Information Quality, 1(1):1–22.

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