One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Knowledge management plays a vital role in supporting enterprise learning, since it facilitates the effective collective intellect of the enterprise. Question Answering (QA) system is playing an important role in current search engine optimization. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting an exact answer. Query languages have complex syntax, requiring a good understanding of the representation schema, including knowledge of details like namespaces, class and property names. In this research we proposed an initial model to implement Conceptual Question Answering and Automatic Information Inferences for the enterprise's operational knowledge management in ontology-based learning organization.