广西师范大学学报（自然科学版） ›› 2023, Vol. 41 ›› Issue (1): 102-112.doi: 10.16088/j.issn.1001-6600.2022031703

• 研究论文 • 上一篇    下一篇

基于卷积推理的多跳知识图谱问答算法

潘海明1, 陈庆锋1*, 邱杰2, 何乃旭1, 刘春雨1, 杜晓敬1   

  1. 1.广西大学计算机与电子信息学院, 广西 南宁 530004;
    2.玉林师范学院计算机科学与工程学院, 广西 玉林 537000
  • 收稿日期:2022-03-17 修回日期:2022-04-19 出版日期:2023-01-25 发布日期:2023-03-07
  • 通讯作者: 陈庆锋(1972—),男,广西柳州人,广西大学教授,博士。E-mail:qingfeng@gxu.edu.cn
  • 基金资助:
    国家自然科学基金(61963004, 61862006); 广西自然科学基金重点项目(2017GXNSFDA198033)

Multi-hop Knowledge Graph Question Answering Based on Convolution Reasoning

PAN Haiming1, CHEN Qingfeng1*, QIU Jie2, HE Naixu1, LIU Chunyu1, DU Xiaojing1   

  1. 1. School of Computer Electronics and Information, Guangxi University, Nanning Guangxi 530004, China;
    2. School of Computer Science and Engineering,Yulin Normal University, Yulin Guangxi 537000, China
  • Received:2022-03-17 Revised:2022-04-19 Online:2023-01-25 Published:2023-03-07

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摘要： 多跳问题相比于简单问题更符合人们日常的提问方式,同时,研究多跳知识图谱问答(KGQA)算法有助于智能问答系统的推广。然而,现有的多跳KGQA方法在2~3跳问题和不完整知识图谱上的答案推理能力较弱。针对这一问题,本文提出基于卷积推理的多跳KGQA算法。首先,为了获取更具表示能力的问题嵌入向量,本文根据问题与关系的语义相似性提出结合字符特征和语义特征的问题嵌入模型;而后,为了增强算法的长链接推理能力,提出基于卷积神经网络(CNN)的答案推理模型来抽取嵌入向量的高阶信息。实验结果显示,相比于已有的5种算法,本文算法在MetaQA数据集的2跳和3跳问题答案预测准确率分别提高了1.7和1.3个百分点,在不完整知识图谱的2跳和3跳问题上分别提高了9.4和9.3个百分点。

关键词: 知识图谱问答, 知识图谱嵌入, 语言模型, 卷积神经网络

Abstract: Compared with simple questions, multi-hop questions are more in line with people's daily questioning methods. At the same time, the research on the multi-hop knowledge graph question-answering (KGQA) algorithm is useful to enhance the intelligent question answering system. However, the existing multi-hop KGQA methods show weak answer reasoning ability in 2 and 3-hop questions and incomplete knowledge graph. To solve this problem, a multi-hop KGQA based on convolution reasoning is proposed in this paper. A question embedding model combining character features and semantic features is developed according to the semantic similarity between questions and relationships to obtain more expressive question embedding. Furthermore, to enhance the long link reasoning ability of the algorithm, an answer reasoning model based on convolutional neural network (CNN) is proposed to extract the high-order information of the embedding. The experimental results on MetaQA dataset demonstrate that compared with the five existing methods, the new algorithm improves the prediction accuracy of the 2-hop and 3-hop questions in the complete knowledge graph and incomplete knowledge graph by 1.7%, 1.3%, 9.4%, and 9.3%, respectively.

Key words: knowledge graph question-answering, knowledge graph embedding, language model, convolutional neural network

中图分类号: 

  • TP391.1
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