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This article examines the structure and components of information storage and retrieval systems and information filtering systems. Analysis of the tasks performed in such selection systems leads to the identification of 13 components. Eight are necessarily present in all such systems, mechanized or not; the others may, but need not be, present. The authors argue that all selection systems can be represented in terms of combinations of these components. The components are of only two types: representations of data objects and functions that operate on them. Further, the functional components, or rules, reduce to two basic types: 1) transformation, making or modifying the members of a set of representations, and 2) sorting or partitioning. The representational transformations may be in the form of copies, excerpts, descriptions, abstractions, or mere identifying references. By partitioning, we mean dividing a set of objects by using matching, sorting, ranking, selecting, and other logically equivalent operations. The typical multiplicity of knowledge sources and of system vocabularies is noted. Some of the implications for the study, use, and design of information storage and retrieval systems are discussed.

There has been a massive investment in the installation of online catalogs: in selection, in the supporting infrastructure of terminals and networks, in catalog record conversion, in training, and, lately, in linking online catalogs with other online systems. In contrast, the state‐of‐the‐art of the functionality of online library catalogs has advanced little in the past few years. Rather it has been a matter of existing systems being upgraded towards the functionality of the better systems and of refinements being added. It is time for a further advance in online catalog design. We believe that the next generation of online catalogs should and will have features such as those discussed and illustrated in this article.

The paper provides a theory base for deriving connotative descriptors for photographs from existing denotative descriptors, and then demonstrates a model for enhancing browsing within image collections by providing a tool for carving up the searching space.

The paper conceptually explores the nature of iconic messages contained in an image by adopting semiotics as a theoretical tool. A problem of image retrieval is identified as loss of connotative messages during the image representation process. The paper proposes an image‐retrieval model utilizing an association thesaurus that facilitates the assignment of connotative index terms by making use of denotative index terms of an image. A series of experiments are performed for evaluating the effectiveness of the proposed model.

Experimental results demonstrate that the association thesaurus improves image‐retrieval effectiveness by increasing the recall of connotatively related image documents as well as the recall of browsing sets.

Applying connotative index terms to an image would be time consuming. Deriving connotative terms from denotative terms and then using them to enrich the browsing environment suggest a method of increasing retrieval effectiveness while reducing the resources required for representation.

Since images are often used to illustrate concepts that are not immediately evident from just the objects in front of the lens, connotative descriptions are particularly valuable. Since human perception of images is, in a sense, hard wired into our brains, browsing is a frequent and reasonable search method in image collections. Using connotative descriptors to point the way to clusters of images with a higher probability of relevance changes the locus of control over representation establishes an environment for dynamic representation, and gives credibility to browsing as a significant search method.