2016/11/26 by Jiacheng Xu, Xu, Jiacheng, Kan Chen +5
Computer Science · #Advanced Graph Neural Networks #Computation and Language (cs.CL) #FOS: Computer and information sciences #Text and Document Classification Technologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1611.08661
openalex publication_date 2016/11/26 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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 by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.