  A sememe is defined as the minimum semantic unit of human languages. Sememe
knowledge bases (KBs), which contain words annotated with sememes, have been
successfully applied to many NLP tasks. However, existing sememe KBs are built
on only a few languages, which hinders their widespread utilization. To address
the issue, we propose to build a unified sememe KB for multiple languages based
on BabelNet, a multilingual encyclopedic dictionary. We first build a dataset
serving as the seed of the multilingual sememe KB. It manually annotates
sememes for over $15$ thousand synsets (the entries of BabelNet). Then, we
present a novel task of automatic sememe prediction for synsets, aiming to
expand the seed dataset into a usable KB. We also propose two simple and
effective models, which exploit different information of synsets. Finally, we
conduct quantitative and qualitative analyses to explore important factors and
difficulties in the task. All the source code and data of this work can be
obtained on https://github.com/thunlp/BabelNet-Sememe-Prediction.
