35 citations · 62 across the 10 of their papers we have counts for
10 papers
Knowledge Graph Embedding: An Overview
Xiou Ge, Yun-Cheng Wang, Bin Wang +1
Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks. These…
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
Yun-Cheng Wang, Xiou Ge, Bin Wang +1
Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by…
CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding
Xiou Ge, Yun-Cheng Wang, Bin Wang +1
Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is pro…
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
Yun-Cheng Wang, Xiou Ge, Bin Wang +1
Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in kno…
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation
Kaitai Zhang, Bin Wang, C. -C. Jay Kuo
A neural network targeting at unsupervised image anomaly localization, called the PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE) network, a density esti…
Task-Specific Dependency-based Word Embedding Methods
Chengwei Wei, Bin Wang, C. -C. Jay Kuo
Two task-specific dependency-based word embedding methods are proposed for text classification in this work. In contrast with universal word embedding methods that work for generic…