  Existing conversational models are handled by a database(DB) and API based
systems. However, very often users' questions require information that cannot
be handled by such systems. Nonetheless, answers to these questions are
available in the form of customer reviews and FAQs. DSTC-11 proposes a three
stage pipeline consisting of knowledge seeking turn detection, knowledge
selection and response generation to create a conversational model grounded on
this subjective knowledge. In this paper, we focus on improving the knowledge
selection module to enhance the overall system performance. In particular, we
propose entity retrieval methods which result in an accurate and faster
knowledge search. Our proposed Named Entity Recognition (NER) based entity
retrieval method results in 7X faster search compared to the baseline model.
Additionally, we also explore a potential keyword extraction method which can
improve the accuracy of knowledge selection. Preliminary results show a 4 \%
improvement in exact match score on knowledge selection task. The code is
available https://github.com/raja-kumar/knowledge-grounded-TODS
