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The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontology-based indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vector-space model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as `bags of words' and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations.