人身保险知识图谱的构建与应用

辅助投保人了解保险产品的条款是保险应用关注的热点问题之一, 借助知识图谱技术辅助人身保险业务开展是一种可行的方法. 本文首先从多源数据中提取并构建人身保险知识图谱LIKG. 具体而言, 构建BERT-IDCNN-BiLSTM-CRF模型提取非结构化文本数据的实体, 通过多种短文本相似度算法以及集成排序算法完成实体对齐; 设计并使用Bootstrapping和分类预测两阶段抽取方法对保险产品进行属性填充. 然后, 根据构建的LIKG, 设计开发原型系统, 该系统使用实体抽取和属性抽取算法提供知识获取功能、设计CF-IIF指标提供属性推荐功能以及实现可视化界面帮助用户快速掌握人身保险产品的信息, 展示LIKG的应用价值.

Construction and Application of Life Insurance Knowledge Graph

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.