Integrated data-model-knowledge representation for natural resource entities. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Ding, Yulin; Xu, Zhaowen; Zhu, Qing; Li, Hankan; Luo, Yan; Bao, Ying; Tang, Lingjun; Zeng, Sen
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 653
• Page End: 678
• Publication Date: 2022-12-31
• Subjects: Natural resources -- data-model-knowledge -- interactional relationships; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2047802
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important. Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources. However, current spatiotemporal data models are based only on data descriptions, attribute records, and other model knowledge of a more general basis, without intuitively describing relationships between these elements and natural resources. This paper, therefore, proposes an integrated data-model-knowledge representation model to explicitly describe the time, space, and interaction of natural resource entities through an integrated knowledge graph. First, this study constructs a conceptual model using the aspects of semantics, scale, and data-model-knowledge, thereby explicitly describing the relationships of natural resources. Second, a logical model of natural resource representation is proposed, that is integrated with time, space, attributes, and relationships. Finally, taking the management of water resources as an example, this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities. The findings of this study lay the foundation for a more efficient, precise, and lucid perception of the distribution laws and complicated interactional relationships of natural resources, both aboveground and underground.