Knowledge graph representation method for semantic 3D modeling of Chinese grottoes

Abstract

The integration of 3D geometric models with semantic information significantly improves the applicability and comprehensibility of cultural heritage. The semantic 3D modeling of Chinese grottoes poses challenges for individuals without expertise in cultural heritage due to gaps in domain knowledge and discrepancies in understanding. However, the existing domain ontology and knowledge graph provide an insufficient representation of the knowledge of Chinese grottoes. To overcome these obstacles, we propose a knowledge graph representation method to provide explicit knowledge for participants at different stages of semantic 3D modeling of Chinese grottoes, which includes schema layer construction and data layer construction. On the schema layer, we design a domain ontology named ChgOnto (Chinese Grottoes Ontology) that consists of four high-level concept classes: spatial object, informational object, digital device, and temporal object. Among the classes in the ChgOnto, the components (e.g., cliff wall, cave roof, cliff wall footing), elements (e.g., primary Buddha statue, pedestal, decoration), the properties (e.g., length, width, depth) of caves and niches in Chinese grottoes as well as the spatial relationships between them are all precisely defined. ChgOnto also reuse the classes from the renowned CIDOC CRM ontology in the cultural heritage field and GeoSPARQL in the geospatial domain, facilitating integration between the two subjects. Considering the schema layer as the conceptual data model, the data layer extracts knowledge from unstructured text through natural language processing tools to instantiate the abstract classes and fill the properties of the schema layer. Finally, the knowledge required for semantic 3D modeling of Chinese grottoes is expressed in the data layer by a knowledge graph in a fixed expression form. Dazu Rock Carvings, a World Heritage site in China, is selected as a case study to validate the practicality and effectiveness of the proposed method. The results reveal that our method offers a robust knowledge-sharing platform for the semantic 3D modeling of Chinese grottoes and demonstrates excellent scalability. The method proposed in this paper can also serve as an informative reference for other types of cultural heritage.