Technology in the field of digital media generates huge amounts of nontextual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user-specified keywords. But many documents convey desired semantic information without containing these keywords. This limitation is frequently addressed through query expansion mechanisms based on the statistical co-occurrence of terms. Recall is increased, but at the expense of deteriorating precision. One can overcome this problem by indexing documents according to context and meaning rather than keywords, although this requires a method of converting words to meanings and the creation of a meaning-based index structure. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontologies. An ontology is a collection of concepts and their interrelationships that provide an abstract view of an application domain. With regard to converting words to meaning, the key issue is to identify appropriate concepts that both describe and identify documents as well as language employed in user requests. This paper describes an automatic mechanism for selecting these concepts. An important novelty is a scalable disambiguation algorithm that prunes irrelevant concepts and allows relevant ones to associate with documents and participate in query generation. We also propose an automatic query expansion mechanism that deals with user requests expressed in natural language. This mechanism generates database queries with appropriate and relevant expansion through knowledge encoded in ontology form. Focusing on audio data, we have constructed a demonstration prototype. We have experimentally and analytically shown that our model, compared to keyword search, achieves a significantly higher degree of precision and recall. The techniques employed can be applied to the problem of information selection in all media types.

Audio, Metadata, Ontology, Precision, Recall, SQL