Semantic Knowledge Graphs for Intercultural and Ethnographic Diversity in the Greater Mekong Subregion

This study aims to design, develop, and evaluate a semantic knowledge graph (KG) and web application that ethically and effectively represents the ethnographic diversity of 375 ethnic groups across Thailand, Laos, Myanmar, Cambodia, Vietnam, and China. Addressing the limitations of conventional Knowledge Organization Systems (KOS), the project integrates semantic web technologies with community-informed data to capture complex relationships involving language, religion, cultural practices, geography, and historical migration. This study employed structured data transformation, entity-relation modeling, and knowledge graph construction using Neo4j. It further incorporated interactive data visualization through React.js and D3.js. The dataset was collaboratively developed by over 20 regional scholars, ensuring cultural relevance and accuracy. Evaluation was conducted through extrinsic and domain-specific validation involving 24 usability experts and 17 ethnographers, who assessed task performance, data completeness, and cultural appropriateness. The findings of this study reveals that the knowledge graph significantly improves usability, interpretability, and task efficiency, exceeding traditional database performance by over 25% in expert evaluations. Additionally, the semantic model supports multilingual access, temporal migration tracking, and culturally contextualized identities. This research concluted that semantically enriched, ethically designed knowledge infrastructures can enhance representation and accessibility of marginalized knowledge. Key limitations include manual data curation and gaps in representing hybrid cultural practices. Future work should focus on scalability through automation and sustained community collaboration. The implications extend to digital humanities, education, and policymaking, offering a replicable model for equitable, culturally sensitive knowledge systems worldwide.