公开(公告)号：US20230351153A1

公开(公告)日：2023-11-02

申请号：US17938058

申请日：2022-10-05

Applicant: Huazhong University of Science and Technology

Inventor： Feng ZHAO ,  Cheng Yan ,  Hai Jin

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0427 ,  G06N3/08 ,  G06N3/0472

Abstract: The present invention relates to knowledge graph reasoning model, system and reasoning method based on Bayesian few-shot learning, wherein the method at least comprises: building a Gaussian mixture model to entities and relations in a knowledge graph so as to reduce uncertainty of the knowledge graph; taking each said entity as a task to simulate a meta-training process of a newly appearing entity in the dynamic knowledge graph and perform task sampling; constructing a meta learner based on a graph neural network and conducing random reasoning; and training the meta learner so as to use a support set to represent the newly appearing entity. The trained knowledge graph reasoning model in the present invention is highly adaptive and able to infer new facts or new entities without retraining.