Grotto Buddhist Niches are important spatial carriers of Buddhist cave art, containing rich architectural, artistic, and historical information. However, image data of these Buddhist niches are fragmented across multiple scales, including visual features, cultural semantics, and spatial structures, which significantly hinders cross-scale correlative analysis. To address this issue, this paper proposes a multi-scale knowledge representation method based on image semantics and ontology. Specifically, we establish a five-tier semantic description model, comprising the visual feature layer, image data layer, entity layer, cultural semantics layer, and relational layer. Furthermore, using Protégé and the classical Seven-Step Method, we develop a domain ontology named Grotto Buddhist Niche Ontology (GBNOnto) to enable unified semantic modeling of multi-scale information. Based on this ontology, a knowledge graph focusing on cave imagery is constructed, with typical caves such as Cave 38 at the Yungang Grottoes selected as case studies. The resulting graph contains 892 entity nodes and 2621 semantic relations, effectively capturing the complex interconnections among architectural typology, artistic characteristics, and cultural semantics within the selected niche instances. The proposed method enables structured and associative integration of multi-scale information in grotto Buddhist niche images. It thus provides a foundational data infrastructure and modeling framework to support effective management, knowledge retrieval, and semantic reasoning.
Wan et al. (Fri,) studied this question.