Knowledge Graph Representation with Jointly Structural and Textual Encoding

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline and obtain state-of-the-art results on link prediction and triplet classification tasks.

Authors

• Jiacheng Xu National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China
• Xipeng Qiu School of Computer Science, Fudan University
• Kan Chen
• Xuanjing Huang School of Computer Science, Fudan University, Shanghai, China Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Shanghai, China

Keywords

• Knowledge Representation, Reasoning, and Logic: Knowledge Representation Languages
• Machine Learning: Deep Learning
• Machine Learning: Knowledge-based Learning
• Natural Language Processing: NLP Applications and Tools