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Titel:

Autocompletion of Design Data in Semantic Building Models using Link Prediction and Graph Neural Networks

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Eisenstadt, Viktor; Bielski, Jessica; Langenhan, Christoph; Althoff, Klaus-Dieter; Langenhan, Christoph; Dengel, Andreas
Abstract:
This paper presents an approach for AI-based autocompletion of graph-based spatial configurations using deep learning in the form of link prediction through graph neural networks. The main goal of the research presented is to estimate the probability of connections between the rooms of the spatial configuration graph at hand using the available semantic information. In the context of early design stages, deep learning-based prediction of spatial connections helps to make the design process...     »
Stichworte:
LOCenter; Spatial Configuration, Autocompletion, Link Prediction, Deep Learning
Herausgeber:
Pak, B; Wurzer, G; Stouffs, R
Kongress- / Buchtitel:
Education and research in Computer Aided Architectural Design in Europe Conference
Band / Teilband / Volume:
1
Verlag / Institution:
KU Leuven Technology Campus, Ghent/Belgium
Verlagsort:
Ghent, Belgium
Jahr:
2022
Seiten:
501-510
Serientitel:
eCAADe
Serienbandnummer:
40
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