  The concepts in knowledge graphs (KGs) enable machines to understand natural
language, and thus play an indispensable role in many applications. However,
existing KGs have the poor coverage of concepts, especially fine-grained
concepts. In order to supply existing KGs with more fine-grained and new
concepts, we propose a novel concept extraction framework, namely MRC-CE, to
extract large-scale multi-granular concepts from the descriptive texts of
entities. Specifically, MRC-CE is built with a machine reading comprehension
model based on BERT, which can extract more fine-grained concepts with a
pointer network. Furthermore, a random forest and rule-based pruning are also
adopted to enhance MRC-CE's precision and recall simultaneously. Our
experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese
CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction
models in KG completion. Particularly, after running MRC-CE for each entity in
CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are
supplied into the KG. The code and datasets have been released at
https://github.com/fcihraeipnusnacwh/MRC-CE
