  The understanding of the human language is quantified by identifying intents
and entities. Even though classification methods that rely on labeled
information are often used for the comprehension of language understanding, it
is incredibly time consuming and tedious process to generate high propensity
supervised datasets. In this paper, we present the generation of accurate
intents for the corresponding Roman Urdu unstructured data and integrate this
corpus in RASA NLU module for intent classification. We embed knowledge graph
with RASA Framework to maintain the dialog history for semantic based natural
language mechanism for chatbot communication. We compare results of our work
with existing linguistic systems combined with semantic technologies. Minimum
accuracy of intents generation is 64 percent of confidence and in the response
generation part minimum accuracy is 82.1 percent and maximum accuracy gain is
96.7 percent. All the scores refers to log precision, recall, and f1 measure
for each intents once summarized for all. Furthermore, it creates a confusion
matrix represents that which intents are ambiguously recognized by approach.
