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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.
[1] 王国维. 王国维遗书. 第四册[M]. 上海: 上海书店出版社, 1983.
[2] Harvard University, Academia Sinica,Peking University, China Biographical Database[EB/OL]. [2018-01-01]..
[3] 刘炜, 叶鹰. 数字人文的技术体系与理论结构探讨[J]. 中国图书馆学报, 2017, 43(5): 32-41.
[3] (Liu Wei, Ye Ying.Exploring Technical System and Theoretical Structure of Digital Humanities[J]. Journal of Library Science in China, 2017, 43(5): 32-41.)
[4] The China Historical Geographic Information System, CHGIS [EB/OL]. [2019-06-13]..
[5] 中华文明之时空基础架构[EB/OL]. [2019-06-13]. .
[5] (Chinese Civilization in Time and Space [EB/OL]. [2019-06-13].
[6] Liu C L, Huang C K, Wang H, et al.Mining Local Gazetteers of Literary Chinese with CRF and Pattern Based Methods for Biographical Information in Chinese History[C]//Proceedings of 2015 IEEE International Conference on Big Data. IEEE, 2015: 1629-1638.
[7] 王东波, 高瑞卿, 沈思, 等. 面向先秦典籍的历史事件基本实体构件自动识别研究[J]. 国家图书馆学刊, 2018(1): 65-77.
[7] (Wang Dongbo, Gao Ruiqing, Shen Si, et al.Research on Automatic Recognition of Basic Entity Component of Historic Events for Pre-Qin Classics[J]. Journal of the National Library of China, 2018(1): 65-77.)
[8] 欧阳剑. 面向数字人文研究的大规模古籍文本可视化分析与挖掘[J]. 中国图书馆学报, 2016, 42(2): 66-80.
[8] (Ouyang Jian.Visual Analysis and Exploration of Ancient Texts for Digital Humanities Research[J]. Journal of Library Science in China, 2016, 42(2): 66-80.)
[9] Allen C, Luo H, Murdock J, et al. Topic Modeling the HànDiăn Ancient Classics[OL]. arXiv Preprint. arXiv:1702.00860, 2017.
[10] Nichols R, Slingerland E, Nielbo K L, et al.Modeling the Contested Relationship Between Analects, Mencius, and Xunzi: Preliminary Evidence from a Machine-Learning Approach[J]. The Journal of Asian Studies, 2018, 77(1): 19-57.
[11] 严承希, 王军. 数字人文视角: 基于符号分析法的宋代政治网络可视化研究[J]. 中国图书馆学报, 2018, 44(5): 87-103.
[11] (Yan Chengxi, Wang Jun.Digital Humanistic Perspective: A Study on the Visualization of Political Network in Song Dynasty Based on Symbolic Analysis[J]. Journal of Library Science in China, 2018, 44(5): 87-103.)
[12] Kyvernitou I, Bikakis A. An Ontology for Gendered Content Representation of Cultural Heritage Artefacts[J/OL]. Digital Humanities Quarterly, 2017, 11(3). .
[13] Chang R Y, Huang C R, Lo F J, et al.From General Ontology to Specialized Ontology: A Study Based on a Single Author Historical Corpus[C]// Proceedings of OntoLex 2005- Ontologies and Lexical Resources, 2005.
[14] Huang C R, Lo F, Chang R Y, et al.Reconstructing the Ontology of the Tang Dynasty: A Pilot Study of the Shakespearean-garden Approach[C]//Proceedings of OntoLex 2004 Workshop. 2004.
[15] Camarda D V, Mazzini S, Antonuccio A.LodLive, Exploring the Web of Data[C]//Proceedings of the 8th International Conference on Semantic Systems. ACM, 2012: 197-200.
[16] 陈涛, 刘炜, 朱庆华. 中文百科概念术语服务平台SinoPedia的构建研究[J].中国图书馆学报, 2018, 44(4): 4-18.
[16] (Chen Tao, Liu Wei, Zhu Qinghua.SinoPedia: An Unified Chinese Terminology Service Platform Based on Linked Data[J]. Journal of Library Science in China, 2018, 44(4): 4-18.)
[17] Faber P, Mairal R, Magaña P.Linking a Domain-Specific Ontology to a General Ontology[C]//Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference. 2011.
[18] Bäumer F S, Gim J, Jeong D H, et al.Linked Open Data System for Scientific Data Sets[C]//Proceedings of the 1st International Workshop on Patent Mining and Its Applications. 2014.
[19] Linked Open Data Platform for EBI Data[EB/OL]. [2018-09-06]..
[20] Jupp S, Malone J, Bolleman J, et al.The EBI RDF Platform: Linked Open Data for the Life Sciences[J]. Bioinformatics, 2014, 30(9): 1338-1339.
[21] Kauppinen T, Baglatzi A, Keßler C.Linked Science: Interconnecting Scientific Assets[A]// Data-Intensive Science[M]. CRC Press, 2012.
[22] Kauppinen T, de Espindola G M, Jones J, et al. Linked Brazilian Amazon Rainforest Data[J]. Semantic Web, 2013, 5(2): 151-155.
[23] Scheider S, Degbelo A, Lemmens R, et al.Exploratory Querying of SPARQL Endpoints in Space and Time[J]. Semantic Web, 2016, 8(1): 65-86.
[24] Klímek J, Helmich J, Necaský M.Application of the Linked Data Visualization Model on Real World Data from the Czech LOD Cloud[C]//Proceedings of LDOW 2014. 2014.
[25] Niepert M, Buckner C, Murdock J, et al.InPhO: A System for Collaboratively Populating and Extending a Dynamic Ontology[C]//Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM, 2008.
[26] 夏翠娟, 刘炜, 陈涛, 等. 家谱关联数据服务平台的开发实践[J]. 中国图书馆学报, 2016, 42(3): 27-38.
[26] (Xia Cuijuan, Liu Wei, Chen Tao, et al.A Genealogy Data Service Platform Implemented with Linked Data Technology[J]. Journal of Library Science in China, 2016, 42(3): 27-38.)
[27] 华东师范大学方志库[EB/OL].[2018-09-05]. .
[27] (Local Annals Database of East China Normal University Library[EB/OL]. [2018-09-05].
[28] 唐诗别苑[EB/OL]. [2018-09-05]. .
[28] (Garden of Tang Poetry[EB/OL]. [2018-09-05].
[29] Heim P, Hellmann S, Lehmann J, et al.RelFinder: Revealing Relationships in RDF Knowledge Bases[C]//Proceedings of International Conference on Semantic and Digital Media Technologies. Springer, Berlin, Heidelberg, 2009: 182-187.
[30] 石泽顺, 肖明. 基于RelFinder的图情学科关联数据语义关系发现实践[J]. 图书情报工作, 2017, 61(17): 139-148.
[30] (Shi Zeshun, Xiao Ming.The Semantic Relation Discovery Practice of Library and Information Science Linked Data Based on RelFinder[J]. Library and Information Service, 2017, 61(17): 139-148.)
[31] GraphDB[EB/OL]. [2018-09-05]. .
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