no code implementations • Findings of the Association for Computational Linguistics 2020 • Katsuhiko Hayashi, Koki Kishimoto, Masashi Shimbo
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP.
no code implementations • 4 Dec 2019 • Koki Kishimoto, Katsuhiko Hayashi, Genki Akai, Masashi Shimbo
Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion. However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large.
2 code implementations • 8 Feb 2019 • Koki Kishimoto, Katsuhiko Hayashi, Genki Akai, Masashi Shimbo, Kazunori Komatani
This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale.