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A lot of images are currently generated in many domains, requiring specialized knowledge of identification and analysis. From one standpoint, many advances have been accomplished in the development of image retrieval techniques based on visual image properties. However, the semantic gap between low-level features and high-level concepts still represents a challenging scenario. On another standpoint, knowledge has also been structured in many fields by ontologies. A promising solution for bridging the semantic gap consists in combining the information from low-level features with semantic knowledge. This work proposes a novel graph-based approach denominated Semantic Interactive Image Retrieval (SIIR) capable of combining Content Based Image Retrieval (CBIR), unsupervised learning, ontology techniques and interactive retrieval. To the best of our knowledge, there is no approach in the …