The three concepts of information science are data, information and knowledge. The structure of one is different from another. The structure of knowledge is more complex than data and information. Knowledge management is complex for traditional information management techniques due to its complex structure and it is difficult to achieve common structure for knowledge captured from heterogeneous sources. Ontology is an upright technology to represent knowledge. Ontology provides homogeneous structure for knowledge acquired from heterogeneous sources. It enables knowledge sharing within and among organizations. Ontology based knowledge management provides a better support for integration of related knowledge sources and searching. The current work proposes an enhanced and clear framework for knowledge management using domain ontology. It addresses major issues of traditional and existing ontology based knowledge management systems. Many Natural Language Processing (NLP) techniques, including stemming, part of-speech tagging, compound recognition, de-compounding, chunking, word sense disambiguation and others, have been used in Information Retrieval (IR). The core IR task we are investigating here is document retrieval. Several other IR tasks use very similar techniques, e.g. document clustering, filtering, new event detection, and link detection, and they can be combined with NLP in a way similar to document retrieval. NLP and IR are very different areas of research, and recent major conferences only have a small number of papers investigating the use of NLP techniques for information retrieval. The moderate success contradicts the intuition that NLP should help IR, which is shared by a large number of researchers. This article reviews the research on combining the two areas and attempts to identify reasons for why NLP has not brought a breakthrough to IR. Natural language processing (NLP) is becoming much more robust and applicable in realistic applications. One area in which NLP has still not been fully exploite d is information retrieval (IR).