|
|||||
|
|
| 融合事实文本的知识库问答方法 | |
| 引用本文： | 王广祥,何世柱,刘康,余正涛,高盛祥,郭军军.融合事实文本的知识库问答方法[J].模式识别与人工智能,2021,34(3):267-274. |
| 作者姓名： | 王广祥 何世柱 刘康 余正涛 高盛祥 郭军军 |
| 作者单位： | 昆明理工大学 信息工程与自动化学院 昆明650504;昆明理工大学 云南省人工智能重点实验室 昆明650500;中国科学院自动化研究所 模式识别国家重点实验室 北京100190 |
| 基金项目： | 国家重点研发计划项目(No.2018YFC0830101,2018YFC0830105,2018YFC0830100);国家自然科学基金项目(No.61533018,61972186,61762056,61472168,61702512);云南省高新技术产业专项(No.201606)资助。 |
| 摘 要： | 在自然语言问题中,由于知识库中关系表达的多样化,通过表示学习匹配知识库问答的答案仍是一项艰巨任务.为了弥补上述不足,文中提出融合事实文本的知识库问答方法,将知识库中的实体、实体类型和关系转换为事实文本,并使用双向Transformer编码器(BERT)进行表示,利用BERT丰富的语义模式得到问题和答案在低维语义空间中的... |
| 关 键 词： | 问答系统 知识库 表示学习 深度学习 |
| 收稿时间： | 2021-01-27 |
Knowledge Base Question Answering Method Incorporating Fact Text |
|
| WANG Guangxiang,HE Shizhu,LIU Kang,YU Zhengtao,GAO Shengxiang,GUO Junjun.Knowledge Base Question Answering Method Incorporating Fact Text[J].Pattern Recognition and Artificial Intelligence,2021,34(3):267-274. | |
| Authors: | WANG Guangxiang HE Shizhu LIU Kang YU Zhengtao GAO Shengxiang GUO Junjun |
| Affiliation: | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504 2. Yunnan Key Laboratory of Artificial Intelligence, Kunming Uni-versity of Science and Technology, Kunming 650500 3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 |
| Abstract: | In natural language problems,the relationship expression in the knowledge base is diversified.Therefore,matching the answers of the knowledge base question and answer through representation learning is still a challenge.To make up the shortcomings,a knowledge base question answering method incorporating fact text is proposed.Entities,entity types and relationships in the knowledge base are converted into fact text.A pre-trained language model(BERT)is employed for representation.The vector of question and answers in low dimensional semantic space is obtained using the rich semantic mode of BERT.The answer with the closest semantic similarity to the question is matched by calculation.Experiments show that the proposed method is effective and robust in answering common simple questions. |
| Keywords: | Question Answering System Knowledge Base Representation Learning Deep Learning |
| 本文献已被 维普 万方数据 等数据库收录！ | |
| 点击此处可从《模式识别与人工智能》浏览原始摘要信息 | |
| 点击此处可从《模式识别与人工智能》下载全文 | |