  Knowledge graphs store a large number of factual triples while they are still
incomplete, inevitably. The previous knowledge graph completion (KGC) models
predict missing links between entities merely relying on fact-view data,
ignoring the valuable commonsense knowledge. The previous knowledge graph
embedding (KGE) techniques suffer from invalid negative sampling and the
uncertainty of fact-view link prediction, limiting KGC's performance. To
address the above challenges, we propose a novel and scalable Commonsense-Aware
Knowledge Embedding (CAKE) framework to automatically extract commonsense from
factual triples with entity concepts. The generated commonsense augments
effective self-supervision to facilitate both high-quality negative sampling
(NS) and joint commonsense and fact-view link prediction. Experimental results
on the KGC task demonstrate that assembling our framework could enhance the
performance of the original KGE models, and the proposed commonsense-aware NS
module is superior to other NS techniques. Besides, our proposed framework
could be easily adaptive to various KGE models and explain the predicted
results.
