Langjunqing Jin

Title: Innovations of Langjunqing Jin

Introduction

Langjunqing Jin is a prominent inventor based in Wuhan, China. He has made significant contributions to the field of knowledge graph embedding and representation learning. With a total of 2 patents, his work focuses on enhancing the accuracy and effectiveness of knowledge graphs.

Latest Patents

Langjunqing Jin's latest patents include a "Relation-enhancement knowledge graph embedding method and system." This invention involves collaborative coordinate-transformation on entities within a knowledge graph and enhances relations through relation-entropy weighting. The method aims to improve the accuracy of fact measurement and reasoning capabilities of the model. Another notable patent is the "Method and device for text-enhanced knowledge graph joint representation learning." This invention focuses on learning structure vector representations based on entity objects and their relations, enhancing semantic expressiveness through a dynamic parameter-generating strategy.

Career Highlights

Langjunqing Jin is affiliated with Huazhong University of Science and Technology, where he contributes to research and development in knowledge graph technologies. His work has garnered attention for its innovative approaches to complex data representation.

Collaborations

He has collaborated with notable colleagues, including Feng Zhao and Hai Jin, who share his passion for advancing knowledge graph methodologies.

Conclusion

Langjunqing Jin's contributions to the field of knowledge graph embedding and representation learning highlight his innovative spirit and dedication to improving data accuracy and reasoning capabilities. His work continues to influence the landscape of knowledge representation technologies.