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Closed domain question answering (QA) systems achieve precision and recall at the cost of complex language processing techniques to parse the answer corpus. We propose a query-based model for indexing answers in a closed domain factoid QA system. Further, we use a phrase term inference method for improving the ranking order of related questions. We posit that a query can be used as the unique identifier of an answer, and thus, the recognition of a query allows us to retrieve the correct answer. In instances where a query is unrecognized, we infer synonymous relationships with other queries through the use of a user feedback loop to improve the ranking order of closely related questions, where possible. The goal of this research is to build a prototype as proof-of-concept that will learn domain-specific knowledge with increased usage through time. This study will focus its efforts in researching the feasibility of a lightweight QA learning system that adapts its responses based on the interaction amongst its users. This offers a lightweight approach to a factoid question answering system for domain specific knowledge bases with significantly simplified language processing techniques.