42 citations · 52 across the 8 of their papers we have counts for
8 papers · 1 filter
Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding
Yushi Hirose, Masashi Shimbo, Taro Watanabe
For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction. They have different…
Binarized Canonical Polyadic Decomposition for Knowledge Graph Completion
Koki Kishimoto, Katsuhiko Hayashi, Genki Akai +1
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-i…
Binarized Knowledge Graph Embeddings
Koki Kishimoto, Katsuhiko Hayashi, Genki Akai +2
Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge grap…
Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion
Hitoshi Manabe, Katsuhiko Hayashi, Masashi Shimbo
Embedding-based methods for knowledge base completion (KBC) learn representations of entities and relations in a vector space, along with the scoring function to estimate the likel…
A Fast and Easy Regression Technique for k-NN Classification Without Using Negative Pairs
Yutaro Shigeto, Masashi Shimbo, Yuji Matsumoto
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for -nearest neighbor (-NN) classification. Unlike Mahalanobis metric learning…
An Algebraic Formalization of Forward and Forward-backward Algorithms
Ai Azuma, Masashi Shimbo, Yuji Matsumoto
In this paper, we propose an algebraic formalization of the two important classes of dynamic programming algorithms called forward and forward-backward algorithms. They are general…