关键词: 知识图谱, 多任务模型, 意图分类, 命名实体识别, 大语言模型

Abstract: Scientific research management is a crucial aspect of university management. However, existing scientific research management systems cannot meet the individual needs of users. This paper focuses on transforming university scientific research management towards intelligence as the demand orientation, and combines knowledge graph, traditional model and large language models to jointly build a new university scientific research management question answering system. Firstly, the scientific research knowledge is collected to build a scientific research knowledge graph. Then, a multi-task model is used for semantic parsing, simultaneously performing intent classification and entity extraction. Finally, the parsing results are used to generate query statements to retrieve information from the knowledge graph and answer general questions. Additionally, large language models are combined with knowledge graph to assist in processing open problems. Experimental results on datasets with associated intents and entities show that the F1 values of the adopted multi-task model in intent classification and entity recognition tasks are 0.958 and 0.937, respectively, surpassing other comparison models and single-task models. The Cypher generation test demonstrates the effectiveness of the custom Prompt in stimulating the emergent abilities of large language models. The accuracy of text-generated Cyphers using large language models reaches 85.8%, effectively handling open questions based on knowledge graph. The accuracy of the question answering system built with knowledge graph, traditional model and large language models is 0.935, which well meets the needs of intelligent question and answer.

Key words: knowledge graph, multi-task model, intent classification, named entity recognition, large language models