Emerging trends in reducing semantic gap towards multimedia access: A comprehensive survey

Abstract

Objectives: The exponential growth of web has increased the volume of the documents. Data does not carry any meaning, but it should be interpreted efficiently for execution of query. Semantic web is combined with statistical and machine learning techniques to increase the efficiency of an information retrieval system. Semantic web supports reasoning of uncertain and vague knowledge for applications like multimedia processing, ubiquitous contexts, etc. Method: Resource Description Framework and Ontology Web Language are standard formats for sharing and integrating knowledge. This paper includes the recent trends in accessing the information in semantic web and reducing semantic gap. Findings: This paper provides an overview of contemporary challenges and open research issues in reducing the Semantic gap. Application/Improvement: Use of Description logics increases the efficiency of semantic retrieval.