Modelling ethnographic diversity with knowledge graphs: a semantic approach to cultural representation in the greater Mekong subregion

This study presents the design, implementation, and evaluation of the Greater Mekong Subregion (GMS) Ethnic Groups Knowledge Graph (KG) and its accompanying web application–an innovative, culturally inclusive platform for modelling the ethnographic diversity of 375 ethnic groups across Thailand, Laos, Myanmar, Cambodia, Vietnam, and China. Addressing the limitations of traditional Knowledge Organization Systems (KOS), the project integrates semantic technologies, local ethnographic data, and community-informed classification frameworks to represent complex relationships involving language, religion, cultural practices, geography, and historical migration of ethnic groups in the GMS. This study employs structured data transformation, entity–relationship modelling, Neo4j-based graph construction, and interactive visualization using React.js and D3.js. Evaluation results, based on extrinsic performance benchmarks and domain-specific expert validation, demonstrate substantial improvements in usability, task efficiency, and data interpretability compared with conventional databases. The findings support using knowledge graphs for ethically grounded, context-sensitive knowledge infrastructures that foster epistemic justice, cultural sustainability, and inclusive access to ethnographic data. In addition, this work contributes a replicable digital humanities model that blends KOS, knowledge management, and semantic web principles to empower interdisciplinary research and community engagement.