Fast Knowledge Graph Completion using Graphics Processing Units
Chun-Hee Lee Dong-oh Kang Hwa Jeon Song
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 $N\times N \times R$ 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.