The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification. 
 Question: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
To capture both high-order structural information of KGs, we used an attention-based embedding propagation method.