收稿：2021-04-28，
纸质出版：2021-12-07
移动端阅览
全球世界文化遗产本体及其环境数据是遗产价值认知和保护的基础，更是大数据时代遗产研究、展示以及可持续利用的依据。本文基于网络和地球大数据，利用自然语言处理、空间分析、领域知识图谱等技术，构建了包括领域知识图谱5万个节点，94万条三元组的全球世界文化遗产知识图谱，支持世界文化遗产的属性、节点关系查询以及图结构表达。基于知识图谱，进行世界文化遗产的景观特征、文化延续性、土地覆被状态以及遗产与城市、社区的空间关系特征分析。研究表明：世界文化遗产具有沿着山脉、水系分布的特点，因此可参考地理单元，形成区域特色的文化遗产管理和保护体系，促进以区域为整体的遗产可持续发展；通过文化的延续性分析可以发现遗产所具有的特定历史文化时代属性，揭示遗产所蕴含的文化多样性，为遗产的整体保护提供新的切入点；遗产和城市与社区的空间关系变化可分为3个阶段：1990年—2000年的匀速靠近期，2000年—2015年的放缓靠近期和2015年—2018年的加速逼近期，因此未来需要持续关注城市和遗产空间关系变化对遗产可持续发展的影响。
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.
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