A Visual Ontology Query Language to Bridge the Semantic Gap in Content-based Image Retrieval 
DNF Awang Iskandar
International Journal of Web Applications, Vol. 1, No. 4, 2009.
DOI: 
Link: http://dline.info/ijwa/fulltext/v1n401.pdf
Abstract:
Interest in the production and potential of digital images has increased greatly in the past decade. The main goal of a CBIR application is to find an image or a set of images that satisfy a userâ€™s information need. This leads to the semantic gap problem, which is the difficulty of relating high level human interpretations with low-level recorded visual features. The synergy between ontology and region-based image annotations is possible to reduce the gap between image features and high-level semantics. Ontology query languages are used to retrieve information stored in ontologies. Unfortunately, users need to learn the syntax before being able to query using current ontology query languages. To overcome this problem, we present the Visual Ontology Query Language (VOQL) that interprets a visual image query into SPARQL. We show that VOQL can be used to retrieve desired images.