计算机工程2025，Vol.51Issue(2)：94-101,8.DOI:10.19678/j.issn.1000-3428.0068433
基于知识库问答的回答生成研究
Research on Answer Generation Based on Knowledge Base Question Answering
摘要
Abstract
Knowledge base question answering aims to use pre-constructed knowledge bases to answer questions raised by users.Existing knowledge base question answering research sorts candidate entities and relationship paths and finally returns the tail entity of the triple as the answer.After the questions provided by the user pass through the entity recognition and entity disambiguation models,they can be linked to candidate entities related to the answers in the knowledge base.Using the generation capability of the language model,the answer can be expanded into a sentence and returned,which is more user-friendly.To improve the generalization ability of the model and compensate for the difference between the question text and structured knowledge,candidate entities and their one-hop relationship subgraphs are organized and input into the generation model through a prompt template,and a popular and fluent text is generated under the guidance of the answer template.Experimental results on the NLPCC 2016 CKBQA and KgCLUE Chinese datasets indicated that on average,the proposed method outperforms the BART-large model by 2.8,2.3,and 1.5 percentage points on the Bilingual Evaluation Understudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)series metrics,respectively.For the Perplexity metric,the method performs comparably to the ChatGPT responses.关键词
知识库问答/提示/实体链接/预训练模型/回答生成Key words
knowledge base question answering/prompt/entity linking/pre-training language model/answer generation分类
信息技术与安全科学引用本文复制引用
饶东宁,许正辉,梁瑞仕..基于知识库问答的回答生成研究[J].计算机工程,2025,51(2):94-101,8.基金项目
广东省自然科学基金面上项目(2021A1515012556) （2021A1515012556）
中山市重大科技专项(2021A1003,2023AJ002) （2021A1003,2023AJ002）
广东省企业科技特派员专项(GDKTP2021025700) （GDKTP2021025700）
广东省本科一流课程资助项目(YLKC202202). （YLKC202202）
