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In this article, we introduce principles for ontology-based querying of information bases. We consider a framework in which a basis ontology over atomic concepts in combination with a concept language defines a generative ontology. Concepts are assumed to be the basis for an index of the information base, in the sense that these concepts are attached to objects in the information base. Concepts are thus applied to obtain a means for descriptions that generalize classical word-based information base indexing. We discuss how the ontology influences the matching of values, especially how the different relations of the ontology may contribute to overall similarity between concepts. Further, we discuss a set of major properties to improve a given similarity measure's accordance with the semantics of the ontology, and use these properties to guide the choice of function. Finally we implement a prototype search system to evaluate the chosen approach.

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In this paper we introduce an approach to exploit knowledge represented in an ontology in answers to queries to an information base. We assume that the ontology is embedded in a knowledge base covering the domain of the information base. The ontology is first of all to influence ranking of objects in answers to queries as measured by similarity to the query. We consider a generative framework where an ontology in combination with a concept language defines a set of well-formed concepts. Wellformed concepts is assumed to be the basis for an indexing of the information base in the sense that these concepts appear as descriptors attached to objects in the base. Concepts are thus applied to obtain a means for descriptions that generalizes simple word-based information base indexing. In effect query evaluation is generalized to be a matter of comparison at the level of concepts rather than words.

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