计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 227-233.doi: 10.11896/jsjkx.220900206

• 人工智能 • 上一篇    下一篇

基于深度哈希学习的知识库问答检索框架

刘铄1, 周刚1,2, 李珠峰1, 吴皓1   

  1. 1 战略支援部队信息工程大学数据与目标工程学院 郑州 450001
    2 数学工程与先进计算国家重点实验室 郑州 450001
  • 收稿日期:2022-09-22 修回日期:2023-01-03 出版日期:2023-11-15 发布日期:2023-11-06
  • 通讯作者: 周刚(gzhougzhou@126.com)
  • 作者简介:(richard_more@163.com)
  • 基金资助:
    河南省科技攻关项目(222102210081)

Deep Hashing-based Retrieval Framework for KBQA

LIU Shuo1 , ZHOU Gang1,2, LI Zhufeng1, WU Hao1   

  1. 1 College of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2022-09-22 Revised:2023-01-03 Online:2023-11-15 Published:2023-11-06
  • About author:LIU Shuo,born in 1998,postgraduate.His main research interests include knowledge graph and data mining.ZHOU Gang,born in 1974,Ph.D,professor.His main research interests include knowledge graph,mass data processing and knowledge graph.
  • Supported by:
    Key Science and Technology Program of Henan Province,China(222102210081).

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摘要： 知识库问答通常包含3个子任务:中心实体识别、实体链接和关系检测。鉴于当前知识库中通常包含数量巨大的实体和关系,为了进一步解决基于复杂规则和倒排索引在知识库中进行检索带来的搜索空间局限性、召回率偏低和难以兼顾语义信息等问题,提出了一种构造知识库问答检索框架的新方法。该框架包含文本召回和哈希召回两个主要模块,通过二次召回设计构成传统文本检索与保留语义信息的哈希码检索的级联检索模式。所提方法在大规模知识库问答测评基准KgCLUE和NLPCC2016提供的数据集上进行实验,结果表明:基于深度哈希学习的知识库问答检索框架可以高效地获取高质量的候选项,在适应大规模知识库的同时能够节省一定的时间开销。

关键词: 检索框架, 知识库问答, 深度哈希学习

Abstract: Question answering over knowledge base usually involves three sub-tasks,topic entity recognition,entity linking and relation detection.Given that the knowledge base usually contains enormous entities and relationships,previous approaches prefer to utilize sophisticated rules and inverted index to retrieve candidate items.In this paper,a new approach is proposed to construct a retrieval framework for question answering over knowledge base to address the problems of search space limitations,low recall and the difficulty to incorporate semantic information demonstrated by previous approach.The framework consists of text retrieve module and hash retrieve module.A cascade retrieve model which contains traditional text retrieve and hash retrieve(semantic information remained) is constructed by recalling twice.The experiment,utilizing the datasets provided by KgCLUE and NLPCC2016,demonstrates that this deep hashing-based retrieve framework can acquire high-quality candidates efficiently and access the knowledge base easily with limited time cost.

Key words: Retrieval framework, Knowledge based question answering, Deep hashing learning

中图分类号: 

  • TP391
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