Abstract
Many databases consist of large collections of “objects” that have complex structure and contain a wide variety of data such as text, numbers, and images. Current database systems can represent objects such as these only with difficulty, and often restrict the type of data that can be stored. In response to these shortcomings, object-oriented database systems have been designed specifically to represent complex objects and accommodate user-defined extensions such as new data types. One of the most important functions that a database system provides is to help users find data with particular characteristics. In object-oriented database systems, this content-based retrieval capability is typically limited to selection from a set of objects based on Boolean combinations of simple predicates. In this paper, we describe a retrieval model based on probabilistic inference that appears to provide the basis of a general retrieval model for complex objects. In particular, it can describe how the meanings of objects are related, including objects in composite object hierarchies, objects referred to by other objects, and multimedia objects.
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