Knowledge Base Question Answering for Intelligent Maintenance of Power Plants

Citations Over Time

Abstract

The maintenance of power plants highly relies upon precious knowledge and experience of handling faults, which is often stored in reports such as the event report. Simple string matching is the traditional means of retrieving relevant reports, and there is a failure of such methods in understanding the user's search intention properly. With a focus on improving the accuracy of information feedback, this work aims to develop a system of knowledge base question answering. Specifically, natural language processing is employed to improve question comprehension and information retrieval. The BiLSTM-CRF model and the fine-tuned BERT model are used to capture named entities and relations in the query. And the BM25 algorithm and the fine-tuned BERT model are combined to develop a scheme for better information retrieval. On this basis, the application interface of knowledge base question answering towards intelligent power plant maintenance is developed. With this question answering system, power plant operators can have better interaction with the knowledge base and improve collaboration.

Related Papers

• → Question Answering over Knowledge Base using Language Model Embeddings(2020)15 cited
• Knowledge-based information retrieval from semi-structured text(1996)
• → Research on the method of knowledge base question answering(2021)2 cited
• → A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base(2017)1 cited
• → Information Retrieval with Language Knowledge(2003)2 cited