Deep representation machine learned model for heterogeneous information networks
US11941057B2 · kind B2 · utility
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Key dates
| Filing date | Jun 1, 2022 |
| Grant date | Mar 26, 2024 |
| Priority date | — |
| Expiry date | Jun 1, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0631
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.
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