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The understanding of the human language is quantified by identifying intentsand entities. Even though classification methods that rely on labeledinformation are often used for the comprehension of language understanding, itis incredibly time consuming and tedious process to generate high propensitysupervised datasets. In this paper, we present the generation of accurateintents for the corresponding Roman Urdu unstructured data and integrate thiscorpus in RASA NLU module for intent classification. We embed knowledge graphwith RASA Framework to maintain the dialog history for semantic based naturallanguage mechanism for chatbot communication. We compare results of our workwith existing linguistic systems combined with semantic technologies. Minimumaccuracy of intents generation is 64 percent of confidence and in the responsegeneration part minimum accuracy is 82.1 percent and maximum accuracy gain is96.7 percent. All the scores refers to log precision, recall, and f1 measurefor each intents once summarized for all. Furthermore, it creates a confusionmatrix represents that which intents are ambiguously recognized by approach.

NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu