Fast Knowledge Graph Completion using Graphics Processing Units

Knowledge graphs can be used in a wide range of areas which require data semantics such as question-answering systems, semantic search systems, and knowledge based systems. A knowledge graph [1, 2, 3] can be constructed using data sources from an open collaboration platform such as wikipedia or wikidata because an enormous amount of information can be gathered in the open collaboration platform. However, the constructed knowledge graph is still incomplete because there can exist a much larger number of potential relations (i.e., N N R, N: the number of entities, R: the number of relation types) compared with the number of relations in the existing knowledge graph and data sources from the open platform intrinsically cannot have all the information to connect the relations. Therefore, we need to add a lot of missing relations (or links) to the knowledge graph. It is called knowledge graph completion. Knowledge graph embedding is one of the most commonly used techniques for knowledge graph completion. Much work [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] has been studied in the literature to improve the accuracy of knowledge graph completion. However, most of the knowledge graph embedding studies do not tackle the running time of the knowledge graph completion. To find a meaningful link (i.e., to add a new relation to the knowledge graph), we should compute the score of each triplet (head, relation, tail) and the number of triplets to be computed is very huge (i.e., N N R, N: is the number of nodes, R is the number of relation types).