Multimedia search and retrieval are considerably improved by providing explicit meaning to visual content by the help of ontologies. Several multimedia ontologies have been proposed recently as suitable knowledge models to narrow the well known semantic gap and to enable the semantic interpretation of images. Since these ontologies have been created in different application contexts, establishing links between them, a task known as ontology matching, promises to fully unlock their potential in support of multimedia search and retrieval. This paper proposes and compares empirically two extensional ontology matching techniques applied to an important semantic image retrieval issue: automatically associating common-sense knowledge to multimedia concepts. First, we extend a previously introduced matching approach to use both textual and visual knowledge. In addition, a novel matching technique based on a multimodal graph is proposed. We argue that the textual and visual modalities have to be seen as complementary rather than as exclusive means to improve the efficiency of the application of an ontology matching procedure in the multimedia domain. An experimental evaluation is included.