An Optimizing Preprocessing Algorithm for Enhanced Web Content

Abstract

In the Web generation, a large amount of structured and unstructured data is present on the Internet. To extract meaningful information from the Web is an extremely difficult task. Preprocessing helps to understand a user query in the IR system. Knowledge extraction is a fundamental method for mining particular knowledge from information retrieval (IR). One of the major challenges determines in current web search vocabulary mismatch problem during the preprocessing. In IR system determine the limitation in web search the search query string is that the relationships between the query expressions and the expanded terms are limited. The query expressions relate to search term fetching information from IR. The expanded terms by adding those terms are most similar to the words of the search string. In this article, we mainly focus on behind user’s search string in Web. We identify the best features within this context for term selection in supervised learning-based model. In this proposed system the main consideration of preprocessing techniques like Tokenization, Stemming, and spell checking and to determine the correct words from the user search string, for the enhanced results for the user.