期刊文献+

辽代历史文化资源知识图谱构建研究 被引量:2

Research on the Construction of Knowledge Graph of Historical and Cultural Resources of the Liao Dynasty
下载PDF
导出
摘要 目前网络上关于辽代历史信息化智能查询服务系统资源相对缺乏,关于辽代历史介绍文本篇幅冗长,不方便大众进行阅读观看。为了便于大众能更加快速准确了解相关的辽代历史知识,基于百度百科、搜狗百科以及基于爬虫技术等形式获取了与辽代历史相关的文本数据,采用BiLSTM-CRF模型进行实体抽取,通过关系抽取模型抽取实体间的关系,通过知识融合等技术对得到的数据进行实体对齐以及本体构建。最终构建辽代历史文化领域知识图谱,并在此知识图谱的基础上开发了可视化查询系统。 It is found that the resources of the intelligent query service system about the historical information of the Liao Dynasty on the Internet are relatively lacking.At the same time,the texts on the Internet about the history of the Liao Dynasty are relatively lengthy and inconvenient for the public to read.In order to facilitate the public to understand the relevant historical knowledge of the Liao Dynasty more quickly and accurately,this study first obtains text data related to the history of the Liao Dynasty based on Baidu Encyclopedia,Sogou Encyclopedia,and crawler technology.Next,the BiLSTM-CRF model is used for entity extraction,and the relationship between entities is extracted through the relationship extraction model.Then,entity alignment and ontology construction are performed on the obtained data through knowledge fusion technology.Finally,a knowledge graph of the history and culture of the Liao Dynasty is constructed,and a visual query system is developed on the basis of this knowledge graph.
作者 刘爽 谭楠楠 杨辉 LIU Shuang;TAN Nan-nan;YANG Hui(School of Computer Science and Engineering, Dalian Minzu University, Dalian Liaoning 116650, China)
出处 《大连民族大学学报》 2021年第1期73-80,共8页 Journal of Dalian Minzu University
基金 辽宁省经济社会发展研究课题(2021lslybkt-022)。
关键词 辽代历史文化 知识图谱 知识抽取 命名实体识别 history and culture of the Liao Dynasty knowledge graph knowledge extraction named entity recognition
  • 相关文献

参考文献11

二级参考文献72

  • 1Zhang Z,Zhang C.A Survey of Recent Advances in Face Detection[EB/OL].[2010].http://research.microsoft.com/apps/pubs/default.aspx?id=132077.
  • 2Ming Y,David K,Narendra A.Detecting Faces in Images:A Survey[J].IEEE Transations on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
  • 3Dalal N,Triggs B.Histograms of Oriented Gradients for Human Detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.USA:San Diego,2005:886-893.
  • 4Ojala T,Pietikainen M,Maenpaa T.Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
  • 5Zulhadi Z,Shahrel S.Face Detection Using Combination of Neural Network and Adaboost[C]//2011 IEEE Region 10 Conference.Indonesia:TENCON,2011:335-338.
  • 6Paul V,Michael J.Robust Real-Time Face Detection[J].International Journal of Computer Vision,2004,57(2):137-154.
  • 7Yow K,Cipolla R.Feature-Based Human Face Detection[J].Image and Vision Computing,1997,15(9):713-735.
  • 8Lanitis A,Taylor C,Cootes T.An Automatic Face Identification System Using Flexible Appearance Models[J].Image and Vision Computing,1995,13(5):393-401.
  • 9Osuna E,Freund R,Girosi F.Training Support Vector Machines:An Application to Face Detection[C]//Proc.IEEE Conf.Computer Vision and Pattern Recognition,1997:130-136.
  • 10Chai D,Ngan N.Locating Facial Region of a Head-and-shoulders Color Image[C]//Third IEEE International Conference Automatic Face and Gesture Recognition,1998:124-129.

共引文献506

同被引文献8

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部