GNNQ: a neuro-symbolic approach to query answering over incomplete knowledge graphs
Real-world knowledge graphs (KGs) are usually incomplete—that is, miss some facts representing valid information. So, when applied to such KGs, standard symbolic query engines fail to produce answers that are expected but not logically entailed by the KGs. To overcome this issue, state-of-the-art ML...
| Main Authors: | , , |
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| Format: | Conference item |
| Sprog: | English |
| Udgivet: |
Springer
2022
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