Explainable GNN-based models over knowledge graphs
Graph Neural Networks (GNNs) are often used to realise learnable transformations of graph data. While effective in practice, GNNs make predictions via numeric manipulations in an embedding space, so their output cannot be easily explained symbolically. In this paper, we propose a new family of GNN-b...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2022
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