知识组织

基于IIIF和语义知识图谱的印章资源整合与知识发现研究

  • 张永娟 ,
  • 刘炜 ,
  • 于建荣 ,
  • 陈涛
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  • 1 上海大学图书情报档案系 上海 200444;
    2 中国科学院上海营养与健康研究所/上海生命科学信息中心 上海 200031;
    3 上海图书馆/上海科学技术情报研究所 上海 200031;
    4 南京大学信息管理学院 南京 210023
张永娟(ORCID:0000-0002-7776-547X),副研究馆员,硕士;刘炜(ORCID:0000-0003-2663-7539),研究员,博士;于建荣(ORCID:0000-0003-0843-1764),研究馆员,硕士。

收稿日期: 2019-08-29

  修回日期: 2019-10-26

  网络出版日期: 2020-04-05

基金资助

本文系中国博士后科学基金项目"数字人文中印章图像的深度知识组织与语义检索"(项目编号:2018M2058)研究成果之一。

Seal Image Resource Integration and Knowledge Discovery Based on IIIF and Semantic Knowledge Graph

  • Zhang Yongjuan ,
  • Liu Wei ,
  • Yu Jianrong ,
  • Chen Tao
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  • 1 Department of Library and Information Systems, Shanghai University, Shanghai 200444;
    2 Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences/Shanghai Information Center of Life Science, Shanghai 200031;
    3 Shanghai Library/Shanghai Institute of Science and Technology Information, Shanghai 200031;
    4 School of Information Management, Nanjing University, Nanjing 210023

Received date: 2019-08-29

  Revised date: 2019-10-26

  Online published: 2020-04-05

摘要

[目的/意义] 数字人文研究的图像资源中蕴含大量信息但利用率极低,不能在异构数据库和不同的应用程序中得到有效的共享与重用,国际图像互操作框架打破了图像资源交换和共享的障碍。[方法/过程] 研究结合国际图像互操作框架和语义知识图谱(关联数据技术)进行图像资源的整合、共享与知识发现,对资源之间的关系进行揭示和知识推理,并通过CNNs算法对图像特征的提取与识别实现基于图像特征的语义检索辅助知识发现。[结果/结论] 提出一套数字人文图像资源整合与知识发现解决方案,并以印章图像资源为应用对象构建"印章知识中心"对以上解决方案的可行性和实践性进行实证检验。

本文引用格式

张永娟 , 刘炜 , 于建荣 , 陈涛 . 基于IIIF和语义知识图谱的印章资源整合与知识发现研究[J]. 图书情报工作, 2020 , 64(7) : 127 -135 . DOI: 10.13266/j.issn.0252-3116.2020.07.015

Abstract

[Purpose/significance] The image resources of digital humanities research contain a lot of information but the utilization rate is extremely low, so it cannot be effectively shared and reused in heterogeneous databases and different applications. The International Image Semantic Interoperability Framework (IIIF) breaks the barriers to image resource exchange and sharing. [Method/process] This study combined IIIF and semantic knowledge graph (linked data technology) to integrate, share and discover knowledge of image resources, reveal the relationship between resources and knowledge reasoning, and it realized semantic retrieval based on image features to assist knowledge discovery by the feature extraction and recognition of image features through CNNs algorithm. [Result/conclusion] Finally, a set of digital human image resource integration and knowledge discovery solutions was proposed, and the "Seal Knowledge Center" was constructed with the seal image resources as the application object to empirically test the feasibility and practicality of the above solutions.

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