  With the development and business adoption of knowledge graph, there is an
increasing demand for extracting entities and relations of knowledge graphs
from unstructured domain documents. This makes the automatic knowledge
extraction for domain text quite meaningful. This paper proposes a knowledge
extraction method based on BERT, which is used to extract knowledge points from
unstructured specific domain texts (such as insurance clauses in the insurance
industry) automatically to save manpower of knowledge graph construction.
Different from the commonly used methods which are based on rules, templates or
entity extraction models, this paper converts the domain knowledge points into
question and answer pairs and uses the text around the answer in documents as
the context. The method adopts a BERT-based model similar to BERT's SQuAD
reading comprehension task. The model is fine-tuned. And it is used to directly
extract knowledge points from more insurance clauses. According to the test
results, the model performance is good.
