Nowadays, the diversity and large deployment of video recorders results in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in the semantic gap between the extracted low-level features and the ground-truth. The ontology paradigm provides a promising solution to overcome this problem. However, no naming syntax convention has been followed in the concept creation step, which constitutes another problem. In this paper, we have considered these two issues and have developed a full video surveillance ontology following a formal naming syntax convention and semantics that addresses queries of both academic research and industrial applications. In addition, we propose an Ontology Video-surveillance Indexing and retrieval System (OVIS) using a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Currently, the existing indexing systems are essentially based on low-level features and the ontology paradigm is used only to support this process with representing surveillance domain. In this paper, we developed the OVIS system based on the SWRL rules and the experiments prove that our approach leads to promising results on the top video evaluation benchmarks and also shows new directions for future developments.