知识组织

基于角色关联的叙事型文化遗产知识表示方法

  • 李旭晖 ,
  • 吴燕秋 ,
  • 王晓光
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  • 1. 武汉大学信息管理学院武汉 430072;
    2. 武汉大学信息资源研究中心武汉 430072
李旭晖(ORCID:0000-0002-1155-3597),副教授,博士,E-mail:lixuhui@whu.edu.cn;吴燕秋(ORCID:0000-0002-2951-2830),硕士研究生;王晓光(ORCID:0000-0003-1284-7164),教授,博士生导师。

收稿日期: 2016-12-29

  修回日期: 2017-04-20

  网络出版日期: 2017-05-05

基金资助

本文系国家自然科学基金重大研究计划“大数据驱动的管理与决策研究”重点支持项目“基于知识关联的金融大数据价值分析、发现及协同创造机制”(项目编号:91646206)和”大数据环境下的知识组织与服务创新”(项目编号:71420107026)和教育部人文社会科学重点研究基地重大项目“大数据资源的语义表示与组织研究-面向文化遗产领域”(项目编号:16JJD870002)研究成果之一。

Research on Knowledge Representation of Narrative Cultural Heritage Based on Role-links

  • Li Xuhui ,
  • Wu Yanqiu ,
  • Wang Xiaoguang
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  • 1. School of Information Management, Wuhan University, Wuhan 430072;
    2. Center for the Study of Information Resources, Wuhan University, Wuhan 430072

Received date: 2016-12-29

  Revised date: 2017-04-20

  Online published: 2017-05-05

摘要

[目的/意义] 叙事型文化遗产具有丰富的隐含知识与语义信息,仅采用图像、文字等数字化形式难以描述故事元素间复杂的关联与模糊语义,因此探究叙事型文化遗产的有效组织与表示具有重大研究意义。[方法/过程] 围绕叙事型文化遗产,利用语义数据模型提出基于角色关联的知识表示方式,构建具有多粒度、多角度且语义流畅的知识表示框架。此方法选取故事中的主要元素作为知识因子,通过聚集与特化的抽象关联关系表示知识因子间的角色关联,形成自然的图结构。[结果/结论] 基于角色关联的知识表示方式具有语义自然、可扩展性强、支持多样化检索等优势,可解决现有知识表示方法存在的角色缺乏情境依赖与表示角度单一问题。

本文引用格式

李旭晖 , 吴燕秋 , 王晓光 . 基于角色关联的叙事型文化遗产知识表示方法[J]. 图书情报工作, 2017 , 61(9) : 116 -122 . DOI: 10.13266/j.issn.0252-3116.2017.09.015

Abstract

[Purpose/significance] It is difficult to describe the complicated association and fuzzy semantics in narrative cultural heritage by only using images or files, because they have rich implicit knowledge and semantic information. Therefore, it is of great significance to explore the effective organization and representation of narrative cultural heritage.[Method/process] This paper constructed a multi-granularity, multi-angle and semantic-fluent knowledge representation framework based on the role-link knowledge representation for the narrative cultural heritage. This representation selected the main elements of the story as knowledge factors. Aggregation and specialization were used to represent the role-link among knowledge factors. Finally, this paper formed the representation structure in a graph.[Result/conclusion] This method has the advantages of semantic nature, strong extensibility, support for diversified retrieval, and also solving problems such as the lack of context dependence, single representation perspective occurring in the existing knowledge representation method.

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