基于知识图谱关系路径的多跳智能问答模型研究

张元鸣; 姬琦; 徐雪松; 程振波; 肖刚

doi:10.12263/DZXB.20230477

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基于知识图谱关系路径的多跳智能问答模型研究

学术论文 | 更新时间：2025-12-08

- 基于知识图谱关系路径的多跳智能问答模型研究
- Knowledge Graph Relation Path Network for Multi-Hop Intelligent Question Answering
- 电子学报 2023年51卷第11期页码：3092-3099
- 作者机构：
  
  浙江工业大学计算机科学与技术学院，浙江杭州 310023
- 作者简介：
  
  [ "张元鸣男，1977年10月出生于河南省濮阳市.现为浙江工业大学计算机学院副教授、硕士生导师.主要研究方向为图神经网络、知识图谱、大数据处理和故障预测等. E-mail: zym@zjut.edu.cn" ]
  [ "姬琦男，1998年9月出生于辽宁省阜新市.2023年毕业于浙江工业大学计算机科学与技术学院.主要研究方向为知识图谱.E-mail: jiqicims@163.com" ]
  [ "徐雪松男，1992年11月出生于安徽省芜湖市.现为浙江工业大学计算机科学与技术学院博士后.主要研究方向为知识工程、智能制造、数字化设计等. E-mail: song885280@zjut.edu.cn" ]
  [ "程振波男， 1975年生于江西省鄱阳县.现为浙江工业大学计算机科学与技术学院副教授、硕士生导师.主要研究方向为强化学习、决策的计算模型和脑启发的智能算法等.E-mail: czb@zjut.edu.cn" ]
  [ "肖刚男，1965 年4月出生于浙江省绍兴市.现为浙江工业大学计算机学院教授、博士生导师.主要研究方向为数据治理、智能制造、数字孪生等.E-mail: xg@zjut.edu.cn" ]
- 基金信息：
  
  浙江省“尖兵”“领雁”研发攻关计划项目(2023C01022);国家自然科学基金(61976193)
- DOI：10.12263/DZXB.20230477
  中图分类号： TP391.1
- 收稿：2023-05-30，
  
  修回：2023-08-27，
  
  纸质出版：2023-11-25
- 稿件说明：
移动端阅览
张元鸣,姬琦,徐雪松等.基于知识图谱关系路径的多跳智能问答模型研究[J].电子学报,2023,51(11):3092-3099.

ZHANG Yuan-ming,JI Qi,XU Xue-song,et al.Knowledge Graph Relation Path Network for Multi-Hop Intelligent Question Answering[J].ACTA ELECTRONICA SINICA,2023,51(11):3092-3099.
张元鸣,姬琦,徐雪松等.基于知识图谱关系路径的多跳智能问答模型研究[J].电子学报,2023,51(11):3092-3099. DOI： 10.12263/DZXB.20230477.

ZHANG Yuan-ming,JI Qi,XU Xue-song,et al.Knowledge Graph Relation Path Network for Multi-Hop Intelligent Question Answering[J].ACTA ELECTRONICA SINICA,2023,51(11):3092-3099. DOI： 10.12263/DZXB.20230477.

摘要

多跳问题是一类通过知识推理才能给出答案的复杂问题，往往需要相关的多项关联知识融合生成最终答案.现有基于知识图谱的多跳智能问答方法推理过程比较复杂，没有考虑关系路径蕴含的结构信息和语义信息.为此，本文提出了基于知识图谱关系路径的多跳智能问答模型，将多跳智能问答问题转换为在低维向量空间中查找知识图谱中最优关系路径的问题.该模型利用表示学习将知识图谱和用户问题同时嵌入到低维的向量空间，实现知识空间和问题空间的统一表示；然后结合主题实体向量表示和问题向量表示对候选实体进行语义评分，产生候选答案集合；以问题实体为起始节点，以候选答案实体为结束节点，从知识图谱中抽取与问题相关的关系路径集合；将关系路径进一步嵌入到低维的向量空间，生成关系路径的向量表示，在向量空间中查找与问题语义匹配度最高的关系路径，最终根据关系路径生成多跳问题的答案.在公开的数据集上对所提出的模型进行了实验，结果表明该方法与现有方法相比不仅具有良好的性能，而且具有良好的稳定性，不会随着问题跳数的增加而降低性能.

Abstract

Complex multi-hop questions require knowledge reasoning to provide answers

which often involves integration of multiple pieces of knowledge to generate final answer. The existing knowledge graph (KG)-based multi-hop intelligent question answering methods often have complicated inference processes and do not consider structural and semantic information embedded in relation paths. To solve this problem

this paper proposes a knowledge graph relation path network for multi-hop intelligent question answering. It transforms the multi-hop intelligent question answering task into an optimization task of finding optimal relation path from KG. In this network

both the KG and question are embedded into low-dimensional vector spaces

and their unified vector representations are obtained. The topic entity and the question entity are combined to perform semantic scoring for generating candidate answers. Starting from the question entity and ending with candidate answers

a set of relation paths relevant to the question from the KG is extracted. The relation paths are further embedded into low-dimensional vector space to generate vector representations. By searching for the relation path with the highest semantic matching degree to the question in the vector space

the answer to the multi-hop question is generated. Experimental results on public datasets show that the proposed method has not only good performance but also good stability compared to the existing methods

and the performance does not decrease with the increase of problem hops.

关键词

Keywords

references

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