In various tasks of knowledge extraction such as relation extraction, community mining and the data extraction
semantic similarity between words is an important challenging task. Manually maintaining ontology will be difficult when the
domain changes over time. For each new word new meanings will be assigned to those words. So automatic methods to
estimate semantic similarity between words using web search interface is required. In existing systems Page counts and snippets
are the two information sources provided by the web search engines. But page counts are not sufficient for measuring semantic
similarity. The method done earlier is SWD(Semantic Word Distance) and it was calculated using page count and the SVM
classifier is used to classify the synonymous and non-synonymous words to find out the semantic similarity among words.
The web documents are retrieved from the query and the feature selection are extracted from the documents. Paper proposes
to use Hierarchical Agglomerative clustering(HAC) to group the documents in to different clusters. The query word will be
passed to the word net to extract the synonym for those words X and Y. The extracted synonym will be compared to the feature
selection of the web documents. Using Naive Bayes classifier the probability value will be calculated for the semantically related
word.