A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language process-ing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and us-ing languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies work-ing on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks includ-ing word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of man-ual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automati-cally predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we sum-marize recent studies on application and expansion of sememe knowledge bases and point out some future directions of re-search on sememes.