This research proposes a transformative framework for advancing cross-linguistic semantic interoperability in language models to foster globally equitable language technologies. Emphasising languages with limited resources, non-standard structures, and diverse cultural contexts, the study navigates beyond the translational aspects of multilingual models. By profiling world languages, investigating equity challenges, and developing ethical guidelines, the research aims to create more inclusive and representative language models. The proposed prototype, incorporating decentralised dataset creation and a novel semantic interoperability model, endeavours to bridge gaps in understanding algorithmic biases. This work seeks to redefine the landscape of Natural Language Processing, prioritising linguistic inclusivity, cultural sensitivity, and global fairness.
