|
|
|
| Knowledge Matching of the Shape of Unearthed Potteries as a Perspective on Digital Humanities |
| Han Muzhe1,2, Gao Jinsong2, Li Yu3, Fu Jiawei2 |
1.Institute of Science and Technology Information, Jiangsu University, Zhenjiang 212013
2.School of Information Management, Central China Normal University, Wuhan 430079
3.School of Archaeology and Museology, Sichuan University, Chengdu 610225 |
|
|
|
|
Abstract Knowledge and effective use of unearthed pottery are of significant importance in revealing the origin and development of Chinese civilization, as typical artifacts in archaeological cultural research are the source of social memory and provide an important clue for constructing cultural identity. Thus, a knowledge-matching method for the shapes of unearthed pottery is proposed in archaeological culture research, which is helpful in promoting the reuse and sharing of knowledge in related fields and can provide a solution to the problem of information redundancy and knowledge mazes in scholarly research. Upon defining the representativeness of unearthed pottery shapes in cultural research and determining the practical significance of knowledge matching, we present a method based on system knowledge from three aspects: knowledge structure analysis, knowledge vector representation, and similarity calculation. In proposing different similarity calculation methods for the types and parts of pottery shapes, the effect of the explicit organization of empirical knowledge in archaeological culture research was considered. Finally, the shape data of unearthed pottery excavated from 20 selected tombs, namely, Chawuhu, Yanbulake, and Mohuchahan Cemeteries, were considered as examples, to verify the effectiveness of the aforementioned method in the study of prehistoric cultural staging. The periodization results of the 20 target tombs were highly consistent (84.21%) with the corrected periodization results proposed by the archaeological community based on pottery type and tomb shape, which fully demonstrates the feasibility and effectiveness of the proposed method, as well as the relevancy in cultural research.
|
|
Received: 20 February 2023
|
|
|
|
1 Petrie W M F. Methods & aims in archaeology[M]. New York: Macmillan, 1904: 16.
2 王巍. 中国考古学大辞典[M]. 上海: 上海辞书出版社, 2014: 7.
3 李济. 西阴村史前的遗存[M]. 北京: 清华学校研究院, 1927.
4 梁思永. 山西西阴村史前遗址的新石器时代的陶器[M]// 三晋考古(第二辑). 太原: 山西人民出版社1996: 287-325, 338-341.
5 Categories for the description of works of art[EB/OL]. (2022-01-24) [2022-10-10]. http://www.getty.edu/research/publications/electronic_publications/cdwa/.
6 Library of Congress. VRA core[EB/OL]. [2022-10-10]. http://www.loc.gov/standards/vracore/.
7 文物数字化保护元数据标准规范征求意见稿发布[EB/OL]. (2017-05-11) [2023-08-10]. https://www.lib.pku.edu.cn/portal/cn/news/0000001494.
8 CIDOC CRM[EB/OL]. [2022-10-10]. https://cidoc-crm.org/.
9 Zhang C X, Cao C G, Gu F, et al. Domain-specific formal ontology of archaeology and its application in knowledge acquisition and analysis[J]. Journal of Computer Science and Technology, 2004, 19(3): 290-301.
10 夏翠娟. 面向人文研究的“数据基础设施”建设——试论图书馆学对数字人文的方法论贡献[J]. 中国图书馆学报, 2020, 46(3): 24-37.
11 陈涛, 刘炜, 单蓉蓉, 等. 知识图谱在数字人文中的应用研究[J]. 中国图书馆学报, 2019, 45(6): 34-49.
12 梁艳琪. 基于关联数据的文物数字资源语义融合与服务研究[D]. 武汉: 华中师范大学, 2017.
13 王晓光, 侯西龙, 程航航, 等. 敦煌壁画叙词表构建与关联数据发布[J]. 中国图书馆学报, 2020, 46(4): 69-84.
14 Dutia K, Stack J. Heritage connector: a machine learning framework for building linked open data from museum collections[J]. Applied AI Letters, 2021, 2(2): e23.
15 夏翠娟. 文化记忆资源的知识融通: 从异构资源元数据应用纲要到一体化本体设计[J]. 图书情报知识, 2021(1): 53-65.
16 吕科. 基于物体轮廓的曲线匹配技术研究[D]. 西安: 西北大学, 2003.
17 王梦婷. 基于卷积神经网络的文物识别系统的研究与实现[D]. 北京: 北京邮电大学, 2020.
18 段继霞. 基于边缘检测算法的文物识别研究与应用[D]. 太原: 中北大学, 2020.
19 孙家泽. 群体智能算法及在三维文物虚拟拼接中的应用[D]. 西安: 西北大学, 2015.
20 曲爱妍, 马乐军. 基于多特征融合技术的文物图像识别方法研究[J]. 金陵科技学院学报, 2021, 37(3): 8-12.
21 韩牧哲, 高劲松, 李钰. 面向考古类型学的出土陶器器形的知识表示与语义关联构建[J]. 图书情报工作, 2022, 66(12): 92-107.
22 张发平, 李丽. 基于多维层次情境模型的业务过程知识推送方法研究[J]. 计算机辅助设计与图形学学报, 2017, 29(4): 751-758.
23 Giunchiglia F, Shvaiko P. Semantic matching[J]. Knowledge Engineering Review, 2003, 18(3): 265-280.
24 Giunchiglia F, Shvaiko P, Yatskevich M. Discovering missing background knowledge in ontology matching[C]// Proceedings of the 17th European Conference on Artificial Intelligence. Amsterdam: IOS Press, 2006: 382-386.
25 Euzenat J, Valtchev P. Similarity-based ontology alignment in OWL-lite[C]// Proceedings of the 16th European Conference on Artificial Intelligence. Amsterdam: IOS Press, 2004: 333-337.
26 Doan A, Halevy A Y. Semantic integration research in the database community: a brief survey[J]. AI Magazine, 2005, 26(1): 83-94.
27 韩姣红. 基于本体的甲骨文文献语义检索模型研究[J]. 图书馆学研究, 2013(7): 51-57.
28 高劲松, 付家炜, 李珂. 敦煌壁画叙词表关联数据实体语义相似度计算方法与实验[J]. 图书情报工作, 2021, 65(8): 97-106.
29 李惠, 陈涛, 侯君明, 等. 钩玄提要——古籍目录智能分析工具构建[J]. 中国图书馆学报, 2021, 47(4): 97-112.
30 Assael Y, Sommerschield T, Shillingford B, et al. Restoring and attributing ancient texts using deep neural networks[J]. Nature, 2022, 603(7900): 280-283.
31 Roueché C. AI minds the gap and fills in missing Greek inscriptions[J]. Nature, 2022, 603(7900): 235-236.
32 Chang X, Chao F, Shang C J, et al. Sundial-GAN: a cascade generative adversarial networks framework for deciphering oracle bone inscriptions[C]// Proceedings of the 30th ACM International Conference on Multimedia. New York: ACM Press, 2022: 1195-1203.
33 俞伟超. 考古类型学的理论与实践[M]. 北京: 文物出版社, 1989.
34 秦小丽, 张萌. 陶器研究的理论与方法[M]. 上海: 复旦大学出版社, 2022.
35 新疆文物考古研究所. 新疆察吾呼——大型氏族墓地发掘报告[M]. 北京: 东方出版社, 1999.
36 高劲松, 韩牧哲. 考古发掘资料图数据库的语义关联构建研究[J]. 图书情报工作, 2021, 65(9): 105-116.
37 韩牧哲, 高劲松, 方晓印, 等. 面向叙事的考古发掘登记资料的语义关联模型构建研究[J/OL]. 数据分析与知识发现, (2023-07-20). https://kns.cnki.net/kcms/detail/10.1478.G2.20230720.1026.002.html.
38 Causevic D. Tree kernels: quantifying similarity among tree-structured data[EB/OL]. [2022-03-20]. https://www.kdnuggets.com/2016/02/tree-kernels-quantifying-similarity-tree-structured-data.html.
39 新疆维吾尔自治区文化厅文物处, 新疆大学历史系文博干部专修班. 新疆哈密焉不拉克墓地[J]. 考古学报, 1989(3): 325-362, 395-402.
40 新疆维吾尔自治区文物考古研究所. 新疆莫呼查汗墓地[M]. 北京: 科学出版社, 2016.
41 邵会秋. 新疆史前时期文化格局的演进及其与周邻文化的关系[M]. 北京: 科学出版社, 2018. |
|
|
|
