Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain, the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.

With the omnipresence of mobile devices coupled with recent advances in automatic speech recognition capabilities, there has been a growing demand for natural language query (NLQ) interface to retrieve information from the knowledge bases. Business users particularly find this useful as NLQ interface enables them to ask questions without the knowledge of the query language or the data schema. In this paper, we apply an existing research technology called ``ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores'' in the industry domain of SAP-ERP systems. The goal is to enable users to query SAP-ERP data using natural language. We present the challenges and their solutions of such a technology transfer. We present the effectiveness of the natural language query interface on a set of questions given by a set of SAP practitioners.