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基于关联标签的非遗图片资源主题发现研究
引用本文:夏立新,白阳,孙晶琼.基于关联标签的非遗图片资源主题发现研究[J].图书情报工作,2016,60(2):22-29.
作者姓名:夏立新  白阳  孙晶琼
作者单位:华中师范大学信息管理学院 武汉 430079
基金项目:本文系国家社会科学基金重点项目"基于语义的馆藏资源深度聚合与可视化展示研究"(项目编号:11AZD090)和华中师范大学优秀博士学位论文培育计划项目"基于社会化标签网络的非遗数字资源主题发现研究"(项目编号:2015YBYB002)研究成果之一。
摘    要:目的/意义]非遗图片资源社会化标签是大众对图片内容的意识反映,蕴含丰富的知识价值。研究标签之间的关联关系有助于构建针对标签的非遗图片资源主题导航,并对非遗图片资源的组织和展示提供参考。方法/过程]从标签关联视角探讨非遗图片资源网站社会化标签之间的关联关系,构建关联标签网络和共标签网络,对其进行社会网络分析和对比,并采用可视化方法对主题特征进行展示。结果/结论]所分析的非遗图片资源社会化标签存在一定主题特征,便于从用户角度组织和利用非遗图片资源,促进非遗文化的传承。

关 键 词:非物质文化遗产  图片资源  社会化标签  关联标签  主题发现  
收稿时间:2015-12-07

Topic Extraction of Intangible Cultural Heritage Image Resources Based on Associated Labels
Xia Lixin,Bai Yang,Sun Jingqiong.Topic Extraction of Intangible Cultural Heritage Image Resources Based on Associated Labels[J].Library and Information Service,2016,60(2):22-29.
Authors:Xia Lixin  Bai Yang  Sun Jingqiong
Institution:School of Information Management, Central China Normal University, Wuhan 430079
Abstract:Purpose/significance] Social tags of intangible cultural heritage image resources reflects the folks' awareness of images contents, and contains abundant knowledge values. The study on the relationships between tags helps to build topic navigations based on tags which provide a reference for the organization and presentation to the intangible cultural heritage image resources.Method/process] This paper explores the relationships between social tags from an image resource site of intangible cultural heritage with the perspective of related tags. Then it builds the related tags network and communal tags network, makes the analysis and comparison on their social network and demonstrates topic features with visualization method.Result/conclusion] The study finds that some topic features appears in the intangible cultural heritage image resources, which can help to organize and use the intangible cultural heritage resources, and promote to inherit the intangible cultural heritage cultures.
Keywords:intangible cultural heritage  image resources  social tag  related tag  topic extraction  
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