公开(公告)号：US20240086731A1

公开(公告)日：2024-03-14

申请号：US18154637

申请日：2023-01-13

Applicant: Huazhong University of Science and Technology

Inventor： Feng ZHAO ,  Mingtao CHEN ,  Kangzheng LIU ,  Hai JIN

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: The present invention relates to a knowledge-graph extrapolating method and system based on multi-layer perception, the method comprising: using relational graph convolutional network encoders to learn embedding representations, and capturing dynamic evolution of a fact; designing emerging task processing units to construct multiple layers of entity sets, and assigning a matching historical relevance; classifying prediction tasks into different reasoning scenes, and connecting them to the corresponding processing unit for partition of entity sets; and using a multi-class task solving method to acquire predicted probability distributions of target entities, and taking the highest one as a prediction answer, so as to accomplish extrapolation of a temporal knowledge graph, wherein the prediction tasks are classified into different reasoning scenes according to whether it contains any entity or relation that has never appeared historically. The knowledge-graph extrapolating system comprises a processor that can run program code information of the disclosed method.