Lexical Knowledge base such as WordNet has been used as a valuable tool for measuring semantic similarity in various Information Retrieval (IR) applications. It is a domain independent lexical database. Since, the quality of semantic relationship in WordNet has not upgraded appropriately for the current usage in the modern IR. Building the WordNet from scratch is not an easy task for keeping updated with current terminology and concepts. Therefore, this paper undergoes a different perspective that automatically updates an existing lexical ontology uses knowledge resources such as the Wikipedia and the Web search engine. This methodology has established the recently evolving relations and also aligns the existing relations between concepts based on its usage over time. It consists of three main phases such as candidate article generation, lexical relationship extraction and generalization and WordNet alignment. In candidate article generation, disambiguation mapping disambiguates ambiguous links between WordNet concepts and Wikipedia articles and returns a set of word-article pairings. Lexical relationship extraction phase includes two algorithms, Lexical Relationship Retrieval (LRR) algorithm discovers the set of lexical patterns exists between concepts and sequential pattern grouping algorithm generalizes lexical patterns and computes corresponding weights based on its frequencies. Furthermore, Sequential Minimal Optimization (SMO) selects the suitable good pattern using the optimal combination of weight of lexical patterns and page count based concurrence measures. WordNet alignment phase establishes a new relationship that is not available in WordNet and also aligns the existing patterns based on computed weight. Experimental results illustrate that the proposed approach better than existing mechanisms on benchmark datasets and achieves a correlation value of 0.87. Moreover, the extended WordNet returns high accuracy results in query expansion.