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数据分析与知识发现  2019, Vol. 3 Issue (6): 109-116    DOI: 10.11925/infotech.2096-3467.2018.1240
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北京大学信息管理系 北京 100871
Visualizing Knowledge Graph of Academic Inheritance in Song Dynasty
Haici Yang,Jun Wang()
Information Management Department, Peking University, Beijing100871, China
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目的】基于中国历代人物传记资料库(CBDB)数据, 利用知识图谱的原理和方法描述宋代文人的学术师承关系, 为人文领域的知识发现提供新的技术实现方式和研究视角。【应用背景】聚焦当前数字化结果可读性较低、难以被直观应用的现状, 通过可视化表达CBDB数据库中的人物关系, 为相关历史研究者和爱好者提供知识发现和探索的数据入口。【方法】构建宋代学术师承本体, 并对数据库中的文人关系数据进行语义化转换。在此基础上开发“宋代学术语义网络”平台展示知识图谱的知识架构和数据内容。【结果】生成的知识图谱中共有5个类, 39个关系, 囊括48 018位人物和6 599条地理信息。“宋代学术语义网络”平台集成了RelFinder可视化工具, 用于检索和动态展示知识图谱中人物、地理实体间的关系。【结论】本研究为CBDB数据的语义化工作提供了理论和实践经验, 为历史学相关问题的研究提供了直观、高效、易用的工具。

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关键词 宋代学术数字人文知识图谱可视化    

[Objective] The study constructs a knowledge graph of academic relationships among scholars in China’s Song Dynasty, aiming to provide new techniques for knowledge exploration in humanity research. [Context] Our study addresses the usability and visualization issues facing digital collections (i.e. the China Biographical Database Project), and establishes a knowledge portal for history researchers and amateurs. [Methods] First, we built the ontology of ancient Chinese scholars. Then, we transformed their relationship to RDF data for the knowledge graph. Finally, we created an online platform to demonstrate the visualization results. [Results] We created the knowledge graph with five classes and 39 relationships based on 48,018 peoples and 6,599 geographic data. The Song’s Academic Inheritance Platform integrates the RelFinder visualization tool to display the entities’ relationships in the knowledge graph. [Conclusions] This study offers practical solutions for semantic research on the China Biographical Database Project and related fields in history.

Key wordsAcademia in Song Dynasty    Digital Humanities    Knowledge Graph    Visualization
收稿日期: 2018-11-08     
杨海慈,王军. 宋代学术师承知识图谱的构建与可视化[J]. 数据分析与知识发现, 2019, 3(6): 109-116.
Haici Yang,Jun Wang. Visualizing Knowledge Graph of Academic Inheritance in Song Dynasty. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1240.
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