Method of and system for explainability for link prediction in knowledge graph
US12014288B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2020 |
| Grant date | Jun 18, 2024 |
| Priority date | — |
| Expiry date | Apr 19, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is provided a method and a system for evaluating a relevance score of a given subset of nodes in a knowledge graph (KG) for purpose of link prediction. An ontology used to generate the KG is obtained and clustered to obtain a set of ontology clusters. A set of vectors having been generated by using an embedding model on the KG is obtained and clustered to obtain a set of vector clusters. Training subgraphs are generated based on the set of ontology clusters and the set of vector clusters by removing subsets of nodes from the KG. Respective prediction models are trained on each training subgraph and ranked based on their link predictions. The relevance score of each removed subset of nodes is determined based on the ranked models. A given subset of nodes is provided as a potential explanation based on the relevance score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.