• VARIKUTI NARESH
• SRINIVAS GADARI

An internet search engine developed an empirical approach for detecting semantic similarity based on page counts and text samples from two queries. It achieves this by combining lexical patterns taken from text excerpts with page counts to provide many word co-occurrence measures. A unique pattern extraction approach and a pattern clustering algorithm are developed to find the numerous semantic links that exist between two provided words. To discover the best mix of co-occurrence metrics based on page counts and lexical pattern clusters, Sequential Minimal Optimization is utilised. In a group mining operation, the proposed approach considerably enhances accuracy. The proposed semantic similarity metric is utilised in a community extraction job to uncover linkages between items, particularly people. With statistically significant accuracy and recall values, the suggested technique outperforms the baselines. The results of the community mining challenge indicate that the suggested method may be used to compare the semantic similarity of not just words, but also named items for which human lexical ontologies are either missing or incomplete.

Measuring semantic similarity between words using Web search engines

"Measuring semantic similarity between words using Web search engines", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.26-31, February-2019, Available :http://www.jetir.org/papers/JETIRFE06006.pdf

"Measuring semantic similarity between words using Web search engines", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp26-31, February-2019, Available at : http://www.jetir.org/papers/JETIRFE06006.pdf