Search Results for author: Masashi Shimbo

Found 19 papers, 3 papers with code

Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding

no code implementations1 Nov 2021 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.

Data Augmentation Knowledge Graph Completion +1

Binarized Canonical Polyadic Decomposition for Knowledge Graph Completion

no code implementations4 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.

A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs

no code implementations IJCNLP 2019 Katsuhiko Hayashi, Masashi Shimbo

Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations.

Knowledge Graph Embedding Knowledge Graphs

Binarized Knowledge Graph Embeddings

2 code implementations8 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.

Knowledge Graph Embeddings Quantization +1

Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion

no code implementations25 Aug 2018 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 likelihood of relations between entities.

Knowledge Base Completion

Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction

1 code implementation ACL 2018 Van-Thuy Phi, Joan Santoso, Masashi Shimbo, Yuji Matsumoto

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction.

Relation Extraction Word Sense Disambiguation

A Fast and Easy Regression Technique for k-NN Classification Without Using Negative Pairs

no code implementations11 Jun 2018 Yutaro Shigeto, Masashi Shimbo, Yuji Matsumoto

This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for $k$-nearest neighbor ($k$-NN) classification.

General Classification Metric Learning +1

Neural Tensor Networks with Diagonal Slice Matrices

no code implementations NAACL 2018 Takahiro Ishihara, Katsuhiko Hayashi, Hitoshi Manabe, Masashi Shimbo, Masaaki Nagata

Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time.

Knowledge Graph Completion Logical Reasoning +2

Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

1 code implementation18 Jun 2017 Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time.

Knowledge Base Completion Transfer Learning

An Algebraic Formalization of Forward and Forward-backward Algorithms

no code implementations22 Feb 2017 Ai Azuma, Masashi Shimbo, Yuji Matsumoto

back propagation) on computation graphs with addition and multiplication, and so on.

On the Equivalence of Holographic and Complex Embeddings for Link Prediction

no code implementations ACL 2017 Katsuhiko Hayashi, Masashi Shimbo

We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al's holographic embedding and Trouillon et al.'s complex embedding.

Knowledge Graph Completion Link Prediction

Ridge Regression, Hubness, and Zero-Shot Learning

no code implementations3 Jul 2015 Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Yuji Matsumoto

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space.

regression Zero-Shot Learning

Developments in the theory of randomized shortest paths with a comparison of graph node distances

no code implementations7 Dec 2012 Ilkka Kivimäki, Masashi Shimbo, Marco Saerens

In particular, we see that the results obtained with the free energy distance are among the best in all the experiments.

Node Clustering

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