计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 251-256.doi: 10.11896/jsjkx.221200080

• 人工智能 • 上一篇    下一篇

融合实体描述的水泥制造领域知识图谱嵌入模型

周泓林, 宋华珠, 张娟   

  1. 武汉理工大学计算机科学与技术学院 武汉430070
  • 收稿日期:2022-12-12 修回日期:2023-06-08 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 宋华珠(shuaz@whut.edu.cn)
  • 作者简介:(zhouhonglin@whut.edu.cn)

Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain

ZHOU Honglin, SONG Huazhu, ZHANG Juan   

  1. School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China
  • Received:2022-12-12 Revised:2023-06-08 Online:2024-03-15 Published:2024-03-13
  • About author:ZHOU Honglin,born in 1999,postgra-duate.Her main research interests include knowledge graph and reinforcement learning.SONG Huazhu,born in 1970,Ph.D,associate professor,master supervisor,is a senior member of CCF(No.12201S).Her main research interests include artificial intelligent and data mining,semantic and knowledge abstraction.

PDF (PC)

110

摘要： 针对知识图谱嵌入模型在进行知识嵌入时大多缺乏对语义信息的考虑,不能很好地提取水泥制造领域专业性的实体语义信息问题,文中将实体描述文本加入到水泥制造领域知识图谱(CMFKG)的嵌入工作中,提出了融合实体描述的知识图谱嵌入模型(KGEED)。该模型采用TransE模型得到CMFKG结构信息的嵌入,采用基于CNN的实体描述嵌入模块获得CMFKG基于语义的嵌入,并用CNN对结构信息嵌入与语义信息嵌入的三元组进行融合,从而可以很好地考虑水泥制造领域知识图谱丰富的实体描述文本信息。经实验表明,该模型在水泥制造领域知识图谱的嵌入工作中取得了不错的效果。

关键词: 水泥制造领域, 知识图谱, 实体描述嵌入, 实体链接

Abstract: To address the problem that many knowledge graph embedding models lack the consideration of semantic information when performing knowledge embedding and cannot extract the semantic information of entities specialized in cement manufactu-ring domain well.The entity description text is added to the embedding work of cement manufacturing domain knowledge graph(CMFKG),and the knowledge graph embedding with entity description model(KGEED) is proposed,which adopts the TransE model to get the embedding of structural information of CMFKG.The CNN-based entity description embedding module is used to obtain the semantic-based embedding of CMFKG,and the triples of structural information embedding and semantic information embedding are fused with CNN,so that the rich entity description text information of the knowledge graph in cement manufactu-ring domain can be well considered.Experiments show that the model achieves good results in the embedding work of the know-ledge graph in cement manufacturing domain.

Key words: Cement manufacturing domain, Knowledge graph, Entity description embedding, Entity link

中图分类号: 

  • TP181
[1]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc,2013:2787-2795.
[2]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2014:1112-1119.
[3]LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2015,291-297.
[4]JI G,HE S,XU L,et al.Knowledge Graph Embedding via Dynamic Mapping Matrix[C]//Processing of the Association for Computational Linguistics & the International Joint Conference on Natural Language.New York:Association for Computational Linguistics,2015:687-696.
[5]ZHANG Z,CAI J,ZHANG Y,et al.Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2020:3065-3072.
[6]ZHANG F,WANG X,LI Z,et al.TransRHS:A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.New York:International Joint Conferences on Artificial Intelligence Organization,2020:2950-2956.
[7]YU L,LUO Z,LIU H,et al.Triplere:Knowledge graph embeddings via tripled relation vectors[J].arXiv:2209.08271,2022.
[8]ZHU Y,LIU H,WU Z,et al.Representation Learning with Ordered Relation Paths for Knowledge Graph Completion[J].ar-Xiv:1909.11864,2019.
[9]MA R X,LI Z Y,CHEN C K,et al.A review of knowledge graph inference research[J].Computer Science,2022,49(S1):74-85.
[10]QU M,CHEN J,XHONNEUX L P,et al.RNNLogic:Learning Logic Rules for Reasoning on Knowledge Graphs[J].arXiv:2010.04029,2020.
[11]QUECOLE F,DUARTE M C,HRUSCHKA E R.Coupling for Coreference Resolution in a Never-ending Learning System[J].Journal of Information and Data Management,2018,9(2):124-134.
[12]JIANG S,LOWD D,DOU D.Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic[C]//Proceedings of IEEE International Conference on Data Mining.New York:IEEE Press,2012:912-917.
[13]CHEN Y,WANG D Z.Knowledge expansion over probabilistic knowledge bases[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data.New York:Association for Computing Machinery,2014:649-660.
[14]CHEN W,XIONG W,YAN X,et al.Variational KnowledgeGraph Reasoning[J].arXiv:1803.06581,2018.
[15]NGUYEN D Q,VU T,NGUYEN T D,et al.Quatre:Relation-aware quaternions for knowledge graph embeddings[C]//Companion Proceedings of the Web Conference.New York:Association for Computing Machinery,2022:189-192.
[16]NGUYEN D Q,VU T,NGUYEN T D,et al.Quatre:Relation-aware quaternions for knowledge graph embeddings[C]//Companion Proceedings of the Web Conference.New York:Association for Computing Machinery,2022:189-192.
[17]XU C,CHEN Y Y,NAYYERI M,et al.Temporal knowledgegraph completion using a linear temporal regularizer and multivector embeddings[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Mexico:Association for Computational Linguistics,2021:2569-2578.
[18]DETTMERS T,MINERVINI P,STENETORP P,et al.Convolutional 2d knowledge graph embeddings[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2018:1-9.
[19]NGUYEN D Q,NGUYEN T D,NGUYEN D Q,et al.A novelembedding model for knowledge base completion based on con-volutional neural network[J].arXiv,2017,1712(02121):1-7.
[20]VASHISHTH S,SANYAL S,NITIN V,et al.Interacte:Improving convolution-based knowledge graph embeddings by increa-sing feature interactions[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2020:3009-3016.
[21]DEMIR C,NGOMO A C N.Convolutional complex knowledge graph embeddings [C]//Proceedings of European Semantic Web Conference.European:Springer Cham,2021:409-424.
[22]XIE R,LIU Z,JIA J,et al.Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2016:2659-2665.
[23]NGUYEN T H,GRISHMAN R.Relation extraction:Perspective from convolutional neural networks[C]//Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing.New York:Association for Computational Linguistics,2015:39-48.
[24]CHEN D.Construction of Knowledge Map in Cement Clinker Production Field [D].Wuhan:Wuhan University of Technology,2020.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发