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 ontologymto 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. Keywords: Semantic image indexing; image annotation; vector space model.