Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals

Galia Weidl, Stefan Berres, Anders L. Madsen, Johannes Daxenberger, Annegret Aulbach
Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:438-451, 2024.

Abstract

The digital transformation of cities inspired the city administration of Aschaffenburg, Germany, to apply artificial intelligence to reduce the significant amount of manual administrative effort needed to evaluate citizens’ ideas for potential future projects. This paper introduces a methodology that combines argument mining with Bayesian networks to evaluate the relative eligibility of city project proposals. The methodology involves two main steps: (1) clustering arguments extracted from public information available on the Internet, and (2) assessing and comparing selected urban issues, planning topics, and citizens’ ideas that have been widely discussed to measure public interest in potential candidate projects. The results of the clustering are fed into a Bayesian network, along with scores for several evaluation criteria, to generate a relative eligibility score. The framework was applied to three candidate projects, resulting in the selection of one of them, while the other two were rejected with a given explanation. The latter motivates the decision and provides transparency to all parties involved in the decision process. The methodology is applicable to other cities after adjustments of criteria.

Cite this Paper

BibTeX
@InProceedings{pmlr-v246-weidl24a, title = {Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals}, author = {Weidl, Galia and Berres, Stefan and Madsen, Anders L. and Daxenberger, Johannes and Aulbach, Annegret}, booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models}, pages = {438--451}, year = {2024}, editor = {Kwisthout, Johan and Renooij, Silja}, volume = {246}, series = {Proceedings of Machine Learning Research}, month = {11--13 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v246/main/assets/weidl24a/weidl24a.pdf}, url = {https://proceedings.mlr.press/v246/weidl24a.html}, abstract = {The digital transformation of cities inspired the city administration of Aschaffenburg, Germany, to apply artificial intelligence to reduce the significant amount of manual administrative effort needed to evaluate citizens’ ideas for potential future projects. This paper introduces a methodology that combines argument mining with Bayesian networks to evaluate the relative eligibility of city project proposals. The methodology involves two main steps: (1) clustering arguments extracted from public information available on the Internet, and (2) assessing and comparing selected urban issues, planning topics, and citizens’ ideas that have been widely discussed to measure public interest in potential candidate projects. The results of the clustering are fed into a Bayesian network, along with scores for several evaluation criteria, to generate a relative eligibility score. The framework was applied to three candidate projects, resulting in the selection of one of them, while the other two were rejected with a given explanation. The latter motivates the decision and provides transparency to all parties involved in the decision process. The methodology is applicable to other cities after adjustments of criteria.} }
Endnote
%0 Conference Paper %T Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals %A Galia Weidl %A Stefan Berres %A Anders L. Madsen %A Johannes Daxenberger %A Annegret Aulbach %B Proceedings of The 12th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2024 %E Johan Kwisthout %E Silja Renooij %F pmlr-v246-weidl24a %I PMLR %P 438--451 %U https://proceedings.mlr.press/v246/weidl24a.html %V 246 %X The digital transformation of cities inspired the city administration of Aschaffenburg, Germany, to apply artificial intelligence to reduce the significant amount of manual administrative effort needed to evaluate citizens’ ideas for potential future projects. This paper introduces a methodology that combines argument mining with Bayesian networks to evaluate the relative eligibility of city project proposals. The methodology involves two main steps: (1) clustering arguments extracted from public information available on the Internet, and (2) assessing and comparing selected urban issues, planning topics, and citizens’ ideas that have been widely discussed to measure public interest in potential candidate projects. The results of the clustering are fed into a Bayesian network, along with scores for several evaluation criteria, to generate a relative eligibility score. The framework was applied to three candidate projects, resulting in the selection of one of them, while the other two were rejected with a given explanation. The latter motivates the decision and provides transparency to all parties involved in the decision process. The methodology is applicable to other cities after adjustments of criteria.
APA
Weidl, G., Berres, S., Madsen, A.L., Daxenberger, J. & Aulbach, A.. (2024). Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals. Proceedings of The 12th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 246:438-451 Available from https://proceedings.mlr.press/v246/weidl24a.html.

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