Fast knowledge graph completion using graphics processing units

• Published in: Journal of parallel and distributed computing 2024-08, Vol.190, p.104885, Article 104885
• Main Authors: Lee, Chun-Hee, Kang, Dong-oh, Song, Hwa Jeon
• Format: Article
• Language: English
• Subjects: GPU processing; Knowledge graph completion; Knowledge graph embedding; Similarity join; TransE
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2024.104885

Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate N×N×R vector operations, where N is the number of entities and R is the number of relation types. It is very costly. In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem. •We tackle the knowledge graph completion problem in terms of the execution time.•We transform the knowledge graph completion problem into the similarity join problem.•We provide a systematic and efficient framework to process it on GPUs.