The ethnic group domain, in particular, is characterized by rich and diverse data sets in the Mekong River Basin (MRB). Ethnic groups’ vocabulary and relevant data come from various sources that cross history, language, and geography. As a result, distinct language is used by specialized groups to characterize their artifacts. Data interoperability among multiple catalogs is highly challenging as a result of this. The usage of controlled vocabularies and thesauri is generally considered a major practice for making preparations for standardization, which is essential for data reuse and sharing. In contrast, when used together, thesauri eliminate ambiguity in natural language, making it easier to identify and integrate data from different sources and allow scholars and computer programs to understand data more efficiently. This paper describes the modeling process of the EGMRB Thesaurus, its integration and role in the infrastructure, its publication as Linked Open Data, and the results of this work after six months of development. This paper presents the rationale behind the realization of this thesaurus. Thesaurus EGMRB (http://thesaurus.asiana.net/vocab/) provides a semantic resource on ethnic groups in the Mekong river basin. EGMRB is the outcome of interdisciplinary cooperation of specialists from the domains of ethnic groups and information science, who collaborated in the context of collaborative research. The thesaurus was developed in Simple Knowledge Organization System (SKOS), a standard data format based on the Resource Description Framework (RDF), using semantic web standard technologies. EGMRB is freely available online, with a SPARQL endpoint (http://thesaurus.asiana.net/vocab/sparql.php) for querying and an API (http://thesaurus.asiana.net/vocab/services.php) for system integration. Digital collections, digital exhibits, and a virtual study environment are being built as part of a digital platform that will give scholars and the general users search and content curation services. EGMRB, which provides unified ideas with related unique and resolvable URIs, can profoundly reduce the barriers to data discovery, integration, and sharing if adopted as a standard and carefully implemented and expanded by the academic community.