The omnipresence of mobile devices coupled with recent advances in automatic speech recognition capabilities has led to a growing demand for natural language querying (NLQ) interfaces to retrieve information from data repositories. Going beyond consumer tools like Siri and Cortana towards industry settings, natural language interaction has been observed to be the next generation user interface to business applications (such as ERP systems) after GUI and touch-based UIs on mobile. It enables business users to ask questions in natural language without needing to have any programming knowledge (such as ABAP or SQL) and knowledge about the data representation mechanisms (such as data schema). State of the art NLQ systems such as ATHENA represents the domain schema in the form of an ontology and performs interpretation using the ontology. The primary challenge in developing a NLQ system for querying data in SAP-ERP is its large ontology which results in an inefficient interpretation. We propose a Steiner tree based novel algorithm which generates a relatively smaller goal-oriented ontology which does not affect the NLQ interpretation. We investigate practical ways to address the problem of precise interpretation generation and introduce an algorithm for Lazy Inclusion. We present the effectiveness of the proposed techniques in the SAP-ERP domain with a set of benchmark natural language questions.