  Commonsense knowledge graph reasoning(CKGR) is the task of predicting a
missing entity given one existing and the relation in a commonsense knowledge
graph (CKG). Existing methods can be classified into two categories generation
method and selection method. Each method has its own advantage. We
theoretically and empirically compare the two methods, finding the selection
method is more suitable than the generation method in CKGR. Given the
observation, we further combine the structure of neural Text Encoder and
Knowledge Graph Embedding models to solve the selection method's two problems,
achieving competitive results. We provide a basic framework and baseline model
for subsequent CKGR tasks by selection methods.
