A Lingual Agnostic Information Retrieval System

The exclusion of monolingual natives from cyberspace is a global socioeconomic and cultural problem. Efforts at addressing this problem have been socioeconomic, culminating in training, empowerment, and digital access with the indelible hurt of language inequities. This paper is aimed at the cyber-inclusion of monolingual natives. Since cyber participation is basically through human interaction with cyber-applications in a human language, encapsulating these applications for interaction in any human language will help evade the hurt of language inequities. Information retrieval system (IRS) remains a fundamental cyber-application. Consequently, adopting the design science research methodology, we introduced a lingual agnostic IRS architecture designed on the principle of transparency on user language detection, information translations, and caching. The detailed design of the architecture was done using the unified modeling language. The designed IRS architecture has been implemented using the agile and component-based software engineering approaches. The resultant lingual agnostic IRS (LAIRS) was evaluated using heuristics and system evaluation methods for parity of language of interaction against the default language and was excellently stable across queries and languages, guaranteeing 86% parity with the default language in the use of other languages for information access and retrieval. Furthermore, it has been shown that LAIRS is the most appropriate IRS to address the problem of language barriers to cyber-inclusion compared with existing IRSs.