Unsupervised feature learning for relational data
US11416469B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 24, 2020 |
| Grant date | Aug 16, 2022 |
| Priority date | — |
| Expiry date | Dec 8, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/284
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.