Classifying phenomena is a central aspect of cognition. Similarly, specifying classes of interest is a central aspect of information systems analysis and design. We extend principles originally developed to guide classification in information systems to the general problem of organizing scientific knowledge. Two fundamental cognitive principles underlie the choice of classes. First, classes should encapsulate inferences about the properties of their instances. Second, collections of classes should provide economy of storage and processing. This leads to a view of classes as carriers of domain knowledge in the form of inferences about situations, rather than containers for information. In this paper, we show how this view, originally developed in the IT context, can be extended to other disciplines, notably the natural sciences. We explain how the principles of inference and economy can guide the choice of individual classes and collections of classes. Moreover, we present a generalized classification-based information processing system (CIPS) model. We propose that scientific theories can be represented by class structures as defined in our model and demonstrate how this can be done by applying CIPS to analyze an example from the philosophy of science literature dealing with nuclear physics. The example demonstrates two advantages of the CIPS approach: first, it can provide a simpler, more scalable, and more informative account of the phenomena than a competing approach (dynamic frames); second, the resolution of inconsistencies between theory and observation can be framed in terms of changes to classification structures, and the principles can even guide such changes.
Parsons, Jeffrey and Wand, Yair
"Extending Classification Principles from Information Modeling to Other Disciplines,"
Journal of the Association for Information Systems: Vol. 14
, Article 2.
Available at: https://aisel.aisnet.org/jais/vol14/iss5/2