重庆大学学报2024，Vol.47Issue(8)：55-64,10.DOI:10.11835/j.issn.1000.582X.2024.08.006
一种融合文本与知识图谱的问答系统模型
A question answering system model integrating text and knowledge graph
摘要
Abstract
Knowledge graph is one of the key technologies to realize question answering in open domain. Open domain question answering tasks often require enough knowledge information,and the incompleteness of knowledge graph becomes an important factor restricting the performance of question answering system. When combining external unstructured text with structured knowledge based on knowledge graphs to fill in missing information,the accuracy and efficiency of retrieving external texts are particularly critical,and selecting texts that are highly relevant to the problem can improve system performance. Conversely,selecting texts that are less relevant to the question will introduce knowledge noise,thereby reducing the accuracy of question answering tasks. Therefore,this paper designs a question answering system model that integrates text and knowledge graph,in which the text retriever can fully mine the semantic information of questions and texts to improve the quality of retrieval and the accuracy of query subgraphs. The knowledge mixer can combine knowledge from text and knowledge bases to build fusion representations of knowledge. The experimental results show that the proposed model has certain advantages in performance compared with the comparison models.关键词
问答系统/知识图谱/外部知识/文本检索/融合表征Key words
question answering system/knowledge graph/external knowledge/text retrieval/fusion representation分类
信息技术与安全科学引用本文复制引用
张佳豪,黄勃,王晨明,曾国辉,刘瑾..一种融合文本与知识图谱的问答系统模型[J].重庆大学学报,2024,47(8):55-64,10.基金项目
科技创新2030"新一代人工智能"重大项目(2020AAA0109300) （2020AAA0109300）
国家自然科学基金青年项目(61802251).Supported by the Scientific and Technological Innovation 2030 Major Project of New Generation Artificial Intelligence(2020AAA0109300),and National Natural Science Foundation of China(61802251). （61802251）
