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Construction and analysis of global world cultural heritage knowledge graph based on big earth data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：liangyongqi18@mails.ucas.ac.cn
  • Introduction：梁勇奇，1997年生，男，硕士研究生，研究方向为数字遗产。E-mail： liangyongqi18@mails.ucas.ac.cn
LIANG Yongqi12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, Beijing 100094, China

  • Email：yangrx@radi.ac.cn
  • Introduction：杨瑞霞，1970年生，女，副研究员，研究方向为数字环境考古、数字遗产。E-mail： yangrx@radi.ac.cn
YANG Ruixia13*,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XIE Yihan12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WANG Pu12,  
  • Affiliation：

    Beijing University of Civil Engineering and Architecture, Beijing 100044, China

YANG Anlin4,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Wei12

Resümee

The global world cultural heritage ont and its environmental data are the basis for the recognition and protection of heritage values, as well as for the research, demonstration and sustainable use of heritage in the age of big data.Based on the network and earth big data, this paper constructs the knowledge map of the world cultural heritage by using the techniques of natural language processing, spatial analysis and domain knowledge map, and analyzes the landscape features, cultural continuity and spatial relationship between site and city and community based on knowledge map.A global world cultural heritage data set including 869 heritage sites and more than 200 types of attributes is reconstructed, and records the rich ontology of world cultural heritage and information about the characteristics of the environment. Then the data were organized in the form of a knowledge map. The research shows that the world cultural heritage site has the characteristics of distribution along the mountains and water systems, so it can refer to the geographical units, form the cultural heritage management and protection system with regional characteristics, and promote the sustainable development of the heritage site as a whole; Changes of the spatial distance between sites and cities or communities can be divided into three phases: the constant proximity period of 1990—2000, the slowdown in 2000—2015 and the acceleration of 2015—2018.The global world cultural heritage data were organized in the form of a knowledge map, which can support multiple attributes associated graph data mining and provide a basis for data reuse and deep processing. Based on the newly constructed data set, we also analyzed and verified the data set and knowledge map from the perspectives of the era and historical process of the heritage, the typical occurrence environment around the heritage, such as the distribution of mountains and water systems, and the changes of cities and communities, so as to meet the needs of global big data monitoring in the protection of world cultural heritage. And changes of the spatial relation between sites and cities show that continued attention needs to be paid to the impact of changes in urban and heritage site spatial relations on the sustainable development of heritage in the future.

Schlüsselwort

remote sensing;cultural heritage;Big Earth Data;Knowledge Graph;Graph-Data Mining;Visualization

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