A Review on Ontology Based Document and Image Retrieval Methods

Objectives: The objective of this critical review is to narrate various approaches that are commonly used in information retrieval which includes documents and images. Methods/Statistical analysis: In early stages for document retrieval, keyword indexing to keyword linking methods has been adapted. These approaches suffered without high-level semantics. Also, this review discusses image retrieval starting from key-word based to ontology based. From literature, it is found that the Content Based Image Retrieval (CBIR) is suffering from semantic gap. The semantic gap is the difference between the human understanding of images and machine interpretation of images. Findings: This work introduces a new method of showing retrieval results in Search Engine results using Object-Attribute-Value (O-A-V) and recommends that it can be applied for document retrieval. Also, an ontology applied image retrieval method is demonstrated to show how ontology is applied to reduce the semantic gap. Application: This review concludes that the ontology applied in information retrieval reduces the semantic gap by adding high-level meaning which improves the presentation of the retrieval systems. This report will help as a roadmap for future researchers in this area. Keywords: Document Retrieval, Image Retrieval, Ontology, Ontology Based Semantic Image Retrieval, Semantic Gap