研究论文

先秦地理志文献跨域知识组织模式研究:以《山海经》为例

  • 林立涛 ,
  • 欧石燕
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  • 南京大学信息管理学院 南京 210023
林立涛,博士研究生;欧石燕,教授,博士,博士生导师,通信作者,E-mail: oushiyan@nju.edu.cn。

收稿日期: 2024-12-06

  修回日期: 2025-03-12

  网络出版日期: 2026-02-09

基金资助

本文系国家社会科学基金一般项目“基于时空知识图谱的史书典籍多源知识融合与多维知识重组研究”(项目编号:24BTQ035)和江苏省研究生科研与实践创新计划项目“基于多源历史文献的地方网络构建与影响力分析研究”(项目编号:KYCX25_0124)研究成果之一。

Cross-Domain Knowledge Organization Patterns in Pre-Qin Geographical Records: A Case Study of the Shan Hai Jing

  • Lin Litao ,
  • Ou Shiyan
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  • School of Information Management, Nanjing University, Nanjing 210023

Received date: 2024-12-06

  Revised date: 2025-03-12

  Online published: 2026-02-09

Supported by

This work is supported by the project of National Social Science Fund of China, titled “Research on Multi-source Knowledge Fusion and Multi-dimensional Knowledge Reorganization of Historical Books and Classics Based on Spatiotemporal Knowledge Graphs” (Grant No. 24BTQ035); and the project of Postgraduate Research & Practice Innovation Program of Jiangsu Province, titled “Construction and Influence Analysis of Place Networks Based on Multi-source Historical Documents” (Grant No. KYCX25_0124).

摘要

[目的/意义] 探索先秦地理志文献中自然地理和人文地理知识的表示、抽取和整合方法,推进古籍数字人文研究中空间信息的规范化组织和应用。[方法/过程] 通过分析先秦地理志文献的内容特征以及借鉴地理信息科学领域的概念体系,对先秦文献《山海经》开展跨域知识组织实证研究。[结果/结论] 提出空间本体与领域本体相结合的先秦地理志文献语义建模方案;构造出《山海经》知识图谱,并对《山海经》中不同空间层级的地理实体及其关联知识进行呈现和解读。研究揭示了《山海经》中的地理知识组织特征,论证了空间信息在跨知识域关联中的关键作用,为历史文献知识组织研究提供空间视角与方法支持。

本文引用格式

林立涛 , 欧石燕 . 先秦地理志文献跨域知识组织模式研究:以《山海经》为例[J]. 图书情报工作, 2026 , 70(3) : 33 -44 . DOI: 10.13266/j.issn.0252-3116.2026.03.004

Abstract

[Purpose/Significance] The study explores methods for the representation, extraction, and integration of natural and human geography knowledge from pre-Qin geographical texts to promote the standardized organization and application of spatial information in digital humanities research of ancient books. [Method/Process] By analyzing the content characteristics of pre-Qin geographical texts and drawing upon conceptual frameworks from geographic information science, empirical cross-domain knowledge organization research was conducted on the pre-Qin text Shan Hai Jing. [Result/Conclusion] A semantic modeling approach combining spatial ontology with domain ontology for pre-Qin geographical texts is proposed; a knowledge graph of Shan Hai Jing is constructed; and geographical entities with their associated knowledge at different spatial levels within Shan Hai Jing are presented and interpreted. The research reveals the geographical knowledge organization characteristics of Shan Hai Jing, demonstrates the key role of spatial information in cross-domain knowledge association, and provides spatial perspectives and methodological support for knowledge organization research of historical texts.

参考文献

[1] 陈刚. “数字人文”与历史地理信息化研究[J]. 南京社会科学, 2014(3): 136-142. (CHEN G. Digital humanities and informationization studies for historical geography [J]. Nanjing journal of social sciences, 2014(3): 136-142.)
[2] 黄水清, 王东波, 何琳. 基于先秦语料库的古汉语地名自动识别模型构建研究[J]. 图书情报工作, 2015, 59(12): 135-140. (HUANG S Q, WANG D B, HE L. Research on constructing automatic recognition model for ancient Chinese place names based on pre-Qin corpus[J]. Library and information service, 2015, 59(12): 135-140.)
[3] GROSSNER K, MOSTERN R. Linked places in world historical gazetteer[C]//Proceedings of the 5th acm sigspatial international workshop on geospatial humanities. New York: Association for computing machinery, 2021: 40-43.
[4] 孙关龙. 中国地理学史上的一次大断裂——兼评《汉书·地理志》[J]. 地球信息科学, 2004(4): 41-44. (SUN G L. A major break in geographical history of China: taking account of geography section of history in the Han dynasty [J]. Geo-information science, 2004(4): 41-44.)
[5] 梁科. 《山经》专名的知识图谱构建及价值分析[D]. 北京: 中国社会科学院研究生院, 2022. (LIANG K. Construction and value analysis of the knowledge graph of proper names in the “Shan Jing”[D]. Beijing: Graduate School of Chinese Academy of Social Sciences, 2022.)
[6] 朱良兵, 蒙飚. 《山海经》知识图谱构建与应用研究[J]. 情报探索, 2023(5): 92-98. (ZHU L B, MENG B. Research on the construction and application of knowledge graph of the classic of mountains and seas [J]. Information research, 2023(5): 92-98.)
[7] 王曼璐. 《山海经》信息可视化设计研究[D]. 秦皇岛: 燕山大学, 2023. (WANG M L. Research on information visualization design of the book of classics of the mountains and sea [D]. Qinhuangdao: Yanshan University, 2023.)
[8] GEUS K, THIERING M. Features of common sense geography: implicit knowledge structures in ancient geographical texts[M]. Zürich: LIT Verlag, 2014.
[9] 肖丽双. 《山海经》方位词研究[D]. 西安: 陕西师范大学, 2014. (XIAO L S. Study on directional terms in the Shan Hai Jing [D]. Xi’an: Shaanxi Normal University, 2014.)
[10] 高劲松, 周树斌, 高颖, 等. 山水志史料资源语义知识关联与多维知识发现研究[J]. 情报资料工作, 2023, 44(5): 82-92. (GAO J S, ZHOU L B, GAO Y, et al. Research on semantic knowledge association and multidimensional knowledge discovery of landscape chronicles historical resources [J]. Information and documentation services, 2023, 44(5): 82-92.)
[11] 胡颖. 家谱GIS中古今地名的时空关系研究[D]. 南京: 南京师范大学, 2008. (HU Y. Spatio-temporal relationships among Chinese ancient and modern placenames oriented to genealogy GIS [D]. Nanjing: Nanjing Normal University, 2008.)
[12] 乐小虬, 杨崇俊, 刘冬林. 空间命名实体的识别[J]. 计算机工程, 2005(20): 49-50, 53. (LE X Q, YANG C J, LIU D L. Spatial named entity recognition in LBS[J]. Computer engineering, 2005(20): 49-50, 53.)
[13] PURVES R S, WINTER S, KUHN W. Places in information science[J]. Journal of the Association for Information Science and Technology, 2019, 70(11): 1173-1182.
[14] 景东升. 基于本体的地理空间信息语义表达和服务研究[D]. 北京: 中国科学院研究生院(遥感应用研究所), 2005. (JING D S. A research on geo-spatial information semantic expression and service based on ontology [D]. Beijing: Graduate University of Chinese Academy of Sciences (Institute of Remote Sensing Applications), 2005.)
[15] SUN K, ZHU Y, PAN P, et al. Geospatial data ontology: the semantic foundation of geospatial data integration and sharing[J]. Big earth data, 2019, 3(3): 269-296.
[16] DBpedia. DBpedia Ontology [EB/OL]. [2025-05-06]. https://dbpedia.org/ontology/.
[17] 刘兴均. “名物”的定义与名物词的确定[J]. 西南师范大学学报(哲学社会科学版), 1998(5): 86-91. (LIU X J. The definition of “MingWu” and the identification of MingWu terms [J]. Journal of Southwest University (social sciences edition), 1998(5): 86-91.)
[18] 苏新春. 现代汉语分类词典[M]. 北京: 商务印书馆, 2013. (SU X C. Modern Chinese classified dictionary [M]. Beijing: The Commercial Press, 2013.)
[19] 刘兴均. 关于“名物”的定义和名物词的界定[J]. 川东学刊, 1998(1): 84-87. (LIU X J. On the definition of “MingWu” and the delimitation of MingWu terms [J]. Sichuan University of Arts and Science Journal, 1998(1): 84-87.)
[20] 贾雯鹤. 《山海经》专名研究[D]. 成都: 四川大学, 2005. (JIA W H. A study of the proper names in the classic of The Mountains and Seas [D]. Chengdu: Sichuan University, 2004.)
[21] Enterprise Integration Lab. iCity ontology: person 1.2 documentation [EB/OL]. [2025-05-06]. https://enterpriseintegrationlab.github.io/icity/Person/Person_1.2/doc/index-en.html.
[22] Explosion AI. spaCy: Industrial-strength natural language processing in python [EB/OL]. [2025-05-06]. https://spacy.io/.
[23] KLIE J C, BUGERT M, BOULLOSA B, et al. The INCEpTION platform: machine-assisted and knowledge-oriented interactive annotation[C]//Proceedings of the 27th international conference on computational linguistics: system demonstrations. Santa Fe, New Mexico, 2018: 5-9.
[24] GONZáLEZ-GALLARDO C E, BOROS E, GIRDHAR N, et al. Yes but. Can ChatGPT identify entities in historical documents?[C]//2023 ACM/IEEE joint conference on digital libraries. Santa Fe: IEEE, 2023: 184-189.
[25] TANG X, SU Q, WANG J, et al. CHisIEC: an information extraction corpus for ancient Chinese history[C]//CALZOLARI N, KAN M Y, HOSTE V, et al. Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation. Torino: ELRA and ICCL, 2024: 3192-3202.
[26] LIU W, FU X, ZHANG Y, et al. Lexicon enhanced Chinese sequence labeling using BERT adapter[C]//ZONG C, XIA F, LI W, et al. Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing. Stroudsburg: Association for Computational Linguistics, 2021: 5847-5858.
[27] ZHANG Y, QI P, MANNING C D. Graph convolution over pruned dependency trees improves relation extraction[C]//RILOFF E, CHIANG D, HOCKENMAIER J, et al. Proceedings of the 2018 conference on empirical methods in natural language processing. Brussels: Association for Computational Linguistics, 2018: 2205-2215.
[28] ZHANG N, DENG S, SUN Z, et al. Attention-based capsule networks with dynamic routing for relation extraction[C]//RILOFF E, CHIANG D, HOCKENMAIER J, et al. Proceedings of the 2018 conference on empirical methods in natural language processing. Brussels: Association for Computational Linguistics, 2018: 986-992.
[29] ZHONG Z, CHEN D. A frustratingly easy approach for entity and relation extraction[C]//TOUTANOVA K, RUMSHISKY A, ZETTLEMOYER L, et al. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies. Stroudsburg: Association for Computational Linguistics, 2021: 50-61.
[30] WANG Y, YU B, ZHANG Y, et al. TPLinker: single-stage joint extraction of entities and relations through token pair linking[C]//SCOTT D, BEL N, ZONG C. Proceedings of the 28th international conference on computational linguistics. Barcelona: International Committee on Computational Linguistics, 2020: 1572-1582.
[31] LU Y, LIU Q, DAI D, et al. Unified structure generation for universal information extraction[C]//MURESAN S, NAKOV P, VILLAVICENCIO A. Proceedings of the 60th annual meeting of the association for computational linguistics. Dublin: Association for Computational Linguistics, 2022: 5755-5772.
[32] WANG D, LIU C, ZHAO Z, et al. GujiBERT and GujiGPT: construction of intelligent information processing foundation language models for ancient texts[EB/OL]. [2025-05-06]. https://arxiv.org/abs/2307.05354.
[33] Baidu AI Studio. Model download page: taskflow information extraction[EB/OL]. [2025-05-06]. https://aistudio.baidu.com/modelsdetail/22/download.
[34] JIA Y. Jiayan: a Chinese classical language processing toolkit[EB/OL]. [2025-05-06]. https://github.com/jiaeyan/Jiayan.
[35] The Apache Software Foundation. Apache Jena: a free and open source Java framework for building semantic Web and Linked data applications [EB/OL]. [2025-05-15]. https://jena.apache.org/.
[36] 刘宗迪. 四海之内:《大荒经》地域考[J]. 文史哲, 2018(6): 40-58, 164. (LIU Z D. Within the four seas: a textual research of the territory described in the Classic of The Great Wilderness[J]. Literature, history, and philosophy, 2018(6): 40-58, 164.)
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