[1]陈新元,谢晟祎,陈庆强,等.结合卷积特征提取和路径语义的知识推理[J].智能系统学报,2021,16(4):729-738.[doi:10.11992/tis.202008007]
 CHEN Xinyuan,XIE Shengyi,CHEN Qingqiang,et al.Knowledge-based inference on convolutional feature extraction and path semantics[J].CAAI Transactions on Intelligent Systems,2021,16(4):729-738.[doi:10.11992/tis.202008007]
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结合卷积特征提取和路径语义的知识推理(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年4期
页码:
729-738
栏目:
学术论文—知识工程
出版日期:
2021-07-05

文章信息/Info

Title:
Knowledge-based inference on convolutional feature extraction and path semantics
作者:
陈新元12 谢晟祎3 陈庆强4 刘羽5
1. 闽江学院 计算机与控制工程学院,福建 福州 350121;
2. 福州墨尔本理工职业学院 信息工程系,福建 福州 350121;
3. 福建农业职业技术学院 教学科研处,福建 福州 350181;
4. 福建工程学院 信息科学与工程学院,福建 福州 350118;
5. 福州墨尔本理工职业学院 现代教育技术中心,福建 福州 350121
Author(s):
CHEN Xinyuan12 XIE Shengyi3 CHEN Qingqiang4 LIU Yu5
1. College of Computer and Control Engineering, Minjiang University, Fuzhou 350121, China;
2. Department of Information Engineering, Fuzhou Melbourne Polytechnic, Fuzhou 350121, China;
3. Teaching and Research Division,, Fujian Vocational College of Agriculture, Fuzhou 350181, China;
4. Information Science and Engineering College, Fujian University of Technology, Fuzhou 350118, China;
5. Modern Education Technical Center, Fuzhou Melbourne Polytechnic, Fuzhou 350121, China
关键词:
知识图谱知识推理嵌入表示路径信息卷积神经网络长短期记忆网络注意力机制链路预测
Keywords:
knowledge graphknowledge inferenceembedding representationpath informationconvolutional neural network (CNN)long-short term memory (LSTM)attention mechanismlink prediction
分类号:
TP391
DOI:
10.11992/tis.202008007
摘要:
传统特征提取方法大多基于嵌入表达,常忽略了路径语义;基于关系路径的推理方法多考虑单一路径,性能仍有提升空间。为进一步提升知识推理能力,使用自定义的卷积神经网络框架编码随机游走生成的多条路径,利用双向长短期记忆网络的隐藏状态合并向量序列,结合注意力机制实现差异化的多路径语义信息集成,计算候选关系与实体对的概率得分,用于判断三元组是否成立。NELL995和FB15k-237数据集上的链路预测结果证明方案可行,F1等指标相比主流模型也有一定优势;进一步在大型数据集和稀疏数据集上验证方案可行。
Abstract:
Embedding-based feature extraction methods usually ignore path semantics; there is still scope of improvement of relational path-based algorithms, which generally consider single paths. To further boost the performance of knowledge-based inferences, a self-defined convolutional neural network framework was employed to encode multiple paths generated by random walks into low-dimensional representations that are merged to form a single vector of hidden states with long-short term memory (LSTM); this is accomplished by combining the attention mechanism-based processes. Semantic information of multiple paths is integrated with various weight distributions used for measuring probability scores of triples comprising candidate relations and entity pairs to determine whether the triples hold or not. Link prediction experiments performed on NELL995 and FB15k-237 demonstrated the capability of the proposed model. Scores of F1 and other indicators also confirmed the advantages of our framework compared with mainstream models. The model was further tested on FC17 and NELL-One.

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备注/Memo

备注/Memo:
收稿日期:2020-08-06。
基金项目:中国高等教育学会2020年度中外合作办学研究课题(ZWHZBX202003)
作者简介:陈新元,讲师,主要研究方向为NLP、知识表达与推理。主持并参与省市级科研课题10余项,主持横向课题多项。发表学术论文10余篇;谢晟祎,高级工程师,主要研究方向为人工智能、机器视觉。参与省级科研课题1项,主持市厅级课题2项。发表学术论文7篇;陈庆强,教授,主要研究方向为图像处理、知识推理。发表学术论文10余篇
通讯作者:陈庆强.E-mail:3204193260@qq.com
更新日期/Last Update: 1900-01-01