In this paper we report an automatic and scalable concept space approach to enhancing the deep searching capability of the NCSA Mosaic. The research, which is based on the findings from a previous NSF National Collaboratory project and which will be expanded in a new Illinois NSF/ARPA/NASA Digital Library project, centers around semantic retrieval and user customization.
Semantic retrieval supports a higher level of abstraction in user search, which can overcome the vocabulary problem for information retrieval. Rather than searching for words within the object space, the search is for terms within a concept space (graph of terms occurring within objects linked to each other by the frequency with which they occur together). Co-occurrence graphs seem to provide good suggestive power in specialized domains, such as biology. By providing a more understandable, system-generated, semantics-rich concept space as an abstraction of the enormously complex object space plus algorithms and interface to assist in object/concept spaces traversal, we believe we can greatly alleviate both information overload and the vocabulary problem of internet services. These techniques will also be used to provide a form of customized retrieval and automatic information routing. Results from past research, the specific algorithms and techniques, and the research plan for enhancing the NCSA Mosaic's search capability in the NSF/ARPA/NASA Digital Library project will be discussed.