Ontology Based Pivoted normalization using Vector Based Approach for information Retrieval

TL;DR

This paper introduces a vector-based, ontology-driven normalization method for information retrieval that leverages data mining principles to enhance document extraction and representation from web data.

Contribution

It presents a novel procedural methodology combining ontology and vector-based statistical approaches for improved document retrieval.

Findings

Effective extraction of relevant documents based on user queries

Enhanced document representation using vector-based statistical methods

Demonstrated applicability to web data analysis

Abstract

The proposed methodology is procedural i.e. it follows finite number of steps that extracts relevant documents according to users query. It is based on principles of Data Mining for analyzing web data. Data Mining first adapts integration of data to generate warehouse. Then, it extracts useful information with the help of algorithm. The task of representing extracted documents is done by using Vector Based Statistical Approach that represents each document in set of Terms.