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An important component of higher level fusion is knowledge discovery. One form of knowledge is a set of relationships between concepts. This paper addresses the automated discovery of ontological knowledge representations such as taxonomies/thesauri from imagery-based data. Multi-target classification is used to transform each source data point into a set of conceptual predictions from a pre-defined lexicon. This classification pre-processing produces co-occurrence data that is suitable for input to an ontology learning algorithm. A neural network with an associative, incremental learning (NAIL) algorithm processes this co-occurrence data to find relationships between elements of the lexicon, thus uncovering the knowledge structure 'hidden' in the dataset. The efficacy of this approach is demonstrated on a dataset created from satellite imagery of a metropolitan region. The flexibility of the NAIL algorithm is illustrated by employing it on an additional dataset comprised of topic categories from a text document collection. The usefulness of the knowledge structure discovered from the imagery data is illustrated via construction of a Bayesian network, which produces an inference engine capable of exploiting the learned knowledge model. Effective automation of knowledge discovery in an information fusion context has considerable potential for aiding the development of machine-based situation awareness capabilities.