Method and device for text-enhanced knowledge graph joint representation learning
US11631007B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 8, 2021 |
| Grant date | Apr 18, 2023 |
| Priority date | — |
| Expiry date | Sep 20, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/289
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention relates to method and device for text-enhanced knowledge graph joint representation learning, the method at least comprises: learning a structure vector representation based on entity objects and their relation linking in a knowledge graph and forming structure representation vectors; discriminating credibility of reliable feature information and building an attention mechanism model, aggregating vectors of different sentences and obtain association-discriminated text representation vectors; and building a joint representation learning model, and using a dynamic parameter-generating strategy to perform joint learning for the text representation vectors and the structure representation vectors based on the joint representation learning model. The present invention selective enhances entity/relation vectors based on significance of associated texts, so as to provide improved semantic expressiveness, and uses 2D convolution operations to train joint representation vectors. As compared to traditional translation models, the disclosed model has better performance in tasks like link prediction and triad classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.