A domain-specific ontology models a specific domain or part of the world. In fact, ontologies have proven to be an excellent medium for capturingpagebreak the knowledge of a domain. We propose an ontology learning scheme in this paper which combines standard multimedia analysis techniques with knowledge drawn from conceptual meta-data to learn a domain-specific multimedia ontology from a set of annotated examples. A standard machine-learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level media features. We construct a more robust and refined version of the basic ontology by learning from this set of conceptually annotated data. We show an application of our ontology-based framework for exploration of multimedia content, in the field of cultural heritage preservation. By constructing an ontology for the cultural heritage domain of Indian classical dance, and by offering an application for semantic annotation of the heritage collection of Indian dance videos, we demonstrate the efficacy of ou approach. © 2012, Springer-Verlag London.