  Understanding the meaning of a text is a fundamental challenge of natural
language understanding (NLU) research. An ideal NLU system should process a
language in a way that is not exclusive to a single task or a dataset. Keeping
this in mind, we have introduced a novel knowledge driven semantic
representation approach for English text. By leveraging the VerbNet lexicon, we
are able to map syntax tree of the text to its commonsense meaning represented
using basic knowledge primitives. The general purpose knowledge represented
from our approach can be used to build any reasoning based NLU system that can
also provide justification. We applied this approach to construct two NLU
applications that we present here: SQuARE (Semantic-based Question Answering
and Reasoning Engine) and StaCACK (Stateful Conversational Agent using
Commonsense Knowledge). Both these systems work by "truly understanding" the
natural language text they process and both provide natural language
explanations for their responses while maintaining high accuracy.
