📝 SummarXiv

Abstract

Recent studies on knowledge graph embedding focus on mapping entities and relations into low-dimensional vector spaces. While most existing models primarily exploit structural information, knowledge graphs also contain rich contextual and textual information that can enhance embedding effectiveness. In this work, we propose a novel model that integrates both contextual and textual signals into entity and relation embeddings through a graph convolutional network. To better utilize context, we introduce two metrics: confidence, computed via a rule-based method, and relatedness, derived from textual representations. These metrics enable more precise weighting of contextual information during embedding learning. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our approach, showing consistent improvements over strong baselines.