AbstractQuestion Answering can be improved by focusing on three areas like the ontology enhanced processing and augmentation, content manipulation approaches, the query and the answer. Ontology enhanced processing could enhance answers to include identified objects satisfying a query. Natural language user questions and information sources with a common ontology are required for ontology-based QA systems. A Question Answering System returns answer to a user question in succinct form. In order to provide a precise answer, the system must know what exactly a user wants. The prior knowledge of the expected answer type helps the Question Answering System to extract correct and precise. Question Answering is one of the major issues in e-learning research on how to provide more interactive activities around the learners and instructors. Every answer to the questions must be relevant to the users query in that context. The input is given to the tree-tagger parser to identify the syntactical information. This syntactical information gives us the lexical constraints like Noun {NN}, Verb {VV} and other terms. The noun and verb keywords are analyzed with the semantic meaning using WordNet and semantic similarity measures. This paper proposes a method for QA system by providing different patterns for the same questions.