文献类型：期刊文献

英文题名：Advancements in Complex Knowledge Graph Question Answering: A Survey

作者：Song, Yiqing[1,2];Li, Wenfa[3];Dai, Guiren[1,2];Shang, Xinna[2]

通讯作者：Shang, XN[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2023

卷号：12

期号：21

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85176588011);WOS:【SCI-EXPANDED(收录号:WOS:001099364100001)】；

基金：The authors would like to express their sincere gratitude to the editors and reviewers for their valuable comments and helpful suggestions, which have greatly enhanced the quality of this paper.

语种：英文

外文关键词：knowledge graph question answering; question answering; complex question; survey

摘要：Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.