Query Classification based Information Retrieval System

Abstract

Information Retrieval (IR) system finds the relevant documents from a large dataset according to the user query. Queries submitted by users to search engines might be ambiguous, concise and their meaning may change over time. As a result, understanding the nature of information that is needed behind the queries has become an important research problem. So, various search engines emphasize the web query classification. For the efficient IR system, this system proposes the Web Query Classification Algorithm (WQCA) by using NoSQL graph database. This system classifies the web queries into each characteristic and each predefined target categories. In web query classification, the input query is first classified into web search taxonomies (characteristics). Then, domain terms are extracted from the query, and each of them is classified into their relevant categories that are stored in the NoSQL database. By using categories from WQCA, this system finds the relevant document from the document collection. The vector space IR model is used in this system to retrieve the relevant document.