The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIX-B4-2026
https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-745-2026
https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-745-2026
04 Aug 2026
 | 04 Aug 2026

A Knowledge Service System for Cultural Heritage Integrating Knowledge Graph and Semantic 3D Model

Jiadong Zhang, Miaole Hou, Jun Chen, Xiaoguang Zhou, Jiaxin Ren, Shunxi Yin, Huiqiang Zhao, Zhihua Zhang, and Bingliang Cui

Keywords: Cultural Heritage, Semantic 3D Model, CityGML, Knowledge Graph, Knowledge Service

Abstract. Cultural heritage (CH) digitization currently suffers from fragmented multi-source heterogeneous data, insufficient knowledge organization, and limited semantic expression in 3D CH models. Existing knowledge graphs and HBIM in CH field lack unified semantic representation and effective GIS integration, thus restricting intelligent knowledge services. To overcome these issues, a knowledge service approach integrating knowledge graph and semantic 3D models is proposed, enabling the transformation from data process to knowledge-driven services. An extension model for CH (CHADE) is developed using the CityGML ADE mechanism to support the construction of semantically enriched 3D geospatial scenes. Meanwhile, A domain ontology (CHOnto) based on CIDOC CRM is constructed to formalize CH knowledge, and multi-source heterogeneous data are organized into a Cultural Heritage Knowledge Graph (CHKG). By establishing semantic connections between knowledge graph and 3D models, the proposed method achieves integrated representation of geometry, spatial context, and domain knowledge. A prototype system (3DCHKS) is implemented and validated through multiple heritage scenarios. Results demonstrate that the approach enhances semantic connectivity, knowledge organization, and scenario-based representation, supporting intuitive visualization and intelligent application. Although limitations remain in generalizability and knowledge extraction robustness, this study provides a novel framework for integrated CH knowledge services and lays a foundation for scalable, knowledge-driven heritage applications.

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