公开(公告)号：US20220147836A1

公开(公告)日：2022-05-12

申请号：US17169869

申请日：2021-02-08

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Feng ZHAO ,  Tao XU ,  Langjunqing JIN ,  Hai JIN

IPC: G06N5/02 ,  G06F40/295 ,  G06N3/08 ,  G06N3/04 ,  G06F40/30

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.

公开(公告)号：US20230297553A1

公开(公告)日：2023-09-21

申请号：US17821633

申请日：2022-08-23

Applicant: Huazhong University of Science and Technology

Inventor： Feng ZHAO ,  Langjunqing JIN ,  Hai Jin

IPC: G06F16/22 ,  G06F16/28 ,  G06F40/30

CPC classification number: G06F16/2228 ,  G06F16/288 ,  G06F40/30

Abstract: The present invention relates to a relation-enhancement knowledge graph embedding method and system, wherein the method at least comprises: performing collaborative coordinate-transformation on entities in the knowledge graph; performing relation core enhancement by means of relation-entropy weighting, so as to endow entity vectors with strong relation property; building an interpretability mechanism for a knowledge graph embedding model, and accounting for effectiveness and feasibility of the relation enhancement by proving convergence of the knowledge graph embedding model; and using a dynamic parameter-adjusting strategy to perform learn representation learning of to the vectors in the knowledge graph, and configuring deviation control to ensure accurate embedding. The present invention can measure rationality of facts with improved accuracy, prove through reasoning the modeling ability of the model from the perspective of complex relation pairs, perform vector computing for entities and relations, thereby accomplishes knowledge graph embedding and reasoning.