  It's ease of use and the availability of browsers for various platforms have
paved the way for the enormous popularity that the World Wide Web currently
enjoys. In the near future, by providing not only easy access to information,
but also means for conducting business transactions, the Web could form the
base technology for the information superhighway. In such a large distributed
information system, resource discovery becomes a critical problem.
  Recent developments in resource discovery systems, such as Harvest and
Whois++, provide scalable mechanisms for the identification, location and
characterization of networked information resources based upon resource
meta-information. However, the Web's vast information space can only be handled
effectively, when resources are meaningfully classified into coherent
conceptual structures.
  The automatic classification of resource meta-information is at the heart of
the WAVE system, which employs methods from the mathematical theory of concept
analysis to analyze and interactively explore the vast information space
defined by wide area resource discovery services. In this paper we discuss
these methods by interpreting various synoptic and summary interchange formats
for resource meta-information, such as the Harvest SOIF and the Whois++ urc, in
terms of basic ideas from concept analysis. In so doing, we advocate concept
analysis as a principled approach to effective resource discovery.
