Artificial intelligence system employing graph convolutional networks for analyzing multi-entity-type multi-relational data
US11823026B2 · kind B2 · utility
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Key dates
| Filing date | Jan 19, 2023 |
| Grant date | Nov 21, 2023 |
| Priority date | — |
| Expiry date | Jan 19, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0202
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.
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