The Q&A system of scientific and technical literature can provide high-level knowledge services for researchers with natural language.But the current semantic parsing-based knowledge graph Q&A system has poor cross-domain adaptability and Q&A systems based on deep learning or large language model suffer from poor interpretability and traceability of results.Aiming to address these issues,this article proposed a Chinese question categorical method based on sentence patterns and designed a Pipeline based framework for the Q&A system of Chinese scientific and technical literature.The experimental results show that question classification based on sentence patterns does not rely on specific domains and its effectiveness is basically comparable to the question classification based on intentions.The Pipeline-based question parsing method can effectively transform questions into knowledge graph query statements and effectively meets users'need for Q&A answers with interpretability and traceability of results.
Q&A system of Chinese scientific and technical literatureknowledge graphquestion categorical methodensemble learning
维普
万方数据
