Trung Kien Tran

Title: Trung Kien Tran: Innovator in Machine Learning and Ontology-Based Data Repositories

Introduction

Trung Kien Tran is a notable inventor based in Ulm, Germany. He has made significant contributions to the fields of machine learning and ontology-based data repositories. With a total of 2 patents, his work focuses on innovative methods that enhance computational efficiency and data organization.

Latest Patents

Trung Kien Tran's latest patents include a "Device and method for machine learning" and "Abstraction refinement for scalable type reasoning in ontology-based data repositories." The first patent describes a computer-implemented method for grouping target entities into clusters. This method involves determining a base association for target entities and establishing inference rules based on this association. The process includes checking whether the difference between the base and altered associations falls below a threshold value, leading to the output or storage of the association of target entities with the clusters. The second patent presents a method for computing a concept materialization of an ontology using a compression technique called 'ABox abstraction and refinement.' This technique significantly reduces the time, memory, and computing resources required for reasoning and materialization.

Career Highlights

Throughout his career, Trung Kien Tran has worked with prominent companies such as Robert Bosch GmbH and Derivo GmbH. His experience in these organizations has allowed him to develop and refine his innovative ideas in machine learning and data management.

Collaborations

Trung Kien Tran has collaborated with talented individuals, including Daria Stepanova and Heike Adel-Vu. These partnerships have contributed to the advancement of his research and the successful development of his patents.

Conclusion

Trung Kien Tran is a distinguished inventor whose work in machine learning and ontology-based data repositories has made a significant impact in the field. His innovative patents and collaborations highlight his commitment to advancing technology and improving computational methods.