Intelligent Information Retrieval Based on Semantics using Data Mining and Ontologies

Abstract

The ever-increasing quantity of material saved and shared on the Web and other document repositories is causing the well-known challenges and issues associated with discovering and effectively managing information in large numbers. Over the last 
ten years, thanks to advancements in search engine technology The collection, storage, and pre-processing of information on a global scale has made remarkable strides, allowing for the immediate return of appropriate resources in response to user demands. Although the desired information may be there in the search area, users may still fail to find it or have to exert a lot of effort to get there. The current state of information retrieval methods relies on keyword-based consolidated content description and query processing, which limits their ability to understand and use user demands and content meanings. A large amount of 
research in the field of information retrieval has focused on the idea of concept based information retrieval, which involves solving the limitations of  based by searching or retrieving by meanings rather than literal strings or keywords. Conceptual search involves the inability to describe relations between search terms. The evolution of information retrieval based on semantics may be aided by semantic technologies like XML and ontology. Exploring the use of semantic technologies like 
XML and ontology to support more expressive queries and more accurate results, this paper aims to develop semantic based information retrieval to intelligently support semantic information retrieval search capabilities in large document repositories. 
In order to accomplish this, we gathered documents from various domains, organized them into tree structures using XML and ontology, and applied data mining techniques like clustering. Then, we used user interests to retrieve information from this structure, which provided the basis for the concept.