Attribute-Driven Capsule Network for Entity Relation Prediction

Abstract

Multi-attribute entity relation prediction is a novel data mining application about designing an intelligent system that supports inferencing across attributes information. However, most existing deep learning methods capture the inner structural information between different attributes are far more limited. In this paper, we propose an attribute-driven approach for entity relation prediction task based on capsule networks that have been shown to demonstrate good performance on relation mining. We develop a self-attention routing method to encapsulate multiple attributes semantic representation into relational semantic capsules and using dynamic routing method to generate class capsules for predicting relations. Due to the lack of multi-attribute entity relation data is a major obstacle in this task, we construct a new real-world multi-attribute entity relation dataset in this work. Experimental results show significant superiority of our model, as compared with other baselines.