Abstract:Assisting users in understanding the clauses of insurance products is one of the hot issues in insurance applications. It is feasible to assist the life insurance business with knowledge graph technology. The life insurance knowledge graph (LIKG) is extracted and constructed by multi-source data. Specifically, the BERT-IDCNN-BiLSTM-CRF model is applied to extract entities from unstructured data, and the entity is aligned by a variety of short text similarity algorithms and ranking ensemble algorithm. A two-stage extraction algorithm is designed to fill the attributes of insurance products by Bootstrapping and classification prediction. Then a prototype system is designed based on LIKG. The system uses the entity extraction and the attribute extraction to provide knowledge acquisition, designs an index called CF-IIF to provide attribute recommendation function, and realizes a visual interface to help users quickly master the information of life insurance, which demonstrates the application value of LIKG.