BERT-based Question Answering using Knowledge Graph Embeddings in Nuclear Power Domain

Abstract

In order to improve the resource utilization rate of existing nuclear power data and promote workers to efficiently obtain the operation information of nuclear power units and assist them in fault diagnosis and maintenance decision-making, this paper constructs a knowledge graph question answering (KGQA) dataset in the field of nuclear power. The BEm-KGQA model based on the pre-trained language model and knowledge graph embedding method was proposed. Our model learns the embedded representation of the knowledge graph through BERT and fine-tunes the BERT model. In the question embedding stage, it learns the embedded representation of the question based on the fine-tuned BERT model. Through experiments, we demonstrate the effectiveness of the method over other models. In addition, this paper implements a nuclear power question answering system. Based on the question answering system, employees can learn about unit information and efficiently obtain information on unusual operating events of nuclear power.