Improving Statistical Multimedia Information Retrieval Model by using Ontology

TL;DR

This paper proposes an improved multimedia information retrieval model that leverages ontology to reduce semantic gaps and enhance relevance in matching user queries with web content.

Contribution

It introduces a novel approach integrating ontology with statistical analysis to improve multimedia IR performance and user satisfaction.

Findings

Reduced semantic gap in multimedia IR

Enhanced relevance in query-document matching

Improved user satisfaction with retrieval results

Abstract

A typical IR system that delivers and stores information is affected by problem of matching between user query and available content on web. Use of Ontology represents the extracted terms in form of network graph consisting of nodes, edges, index terms etc. The above mentioned IR approaches provide relevance thus satisfying users query. The paper also emphasis on analyzing multimedia documents and performs calculation for extracted terms using different statistical formulas. The proposed model developed reduces semantic gap and satisfies user needs efficiently.