Development of graph-based management tool based on Graph Neural Networks
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Date
2021
Publication Type
Master Thesis
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yes
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Abstract
Knowledge Graphs, a form of connected data, created a new research field to
apply machine learning algorithms called Graph Neural Networks (GNN). We
study several GNN models by integrating them into a newly proposed framework
called GrafAE, introducing multiple novel GNN models. Furthermore, in this
work, we research the effect of embedding dimensionality, a vector representation
of the knowledge graphs’ features, and the impact of embedding initialization.
Finally, we report several improvements on the studied GNN models and propose
an enhancement to a GNN that yields state-of-the-art results with the original
evaluation method which may return too high performance and could benefit
from a new evaluation process.
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Examiner : Giovannini, Andrea
Examiner : Renggli, Cedric
Examiner : Zhang, Ce
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ETH Zurich
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09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)