|Expanding Access to Science and Technology (UNU, 1994, 462 pages)|
|Session 3: New technologies and media for information retrieval and transfer|
|Information retrieval: Theory, experiment, and operational systems|
It may be noted that I have not yet mentioned any of the work in the artificial intelligence (AI), expert system or knowledge-based system (KBS) areas. There have indeed been many attempts to apply such ideas to information retrieval, though there is in my view less evidence for their effect or effectiveness in the context of operational systems.
The possible role(s) for knowledge bases in IR is the subject of much debate. One approach is to treat the expert intermediary as the source of knowledge, in other words to try to encapsulate the intermediary's skill in a system . However, a major component of the intermediary's expertise, at least as represented in such systems, seems to be the manipulation of Boolean search statements. If we can get by without such statements, then much of the point of these systems seems to be lost.
The other kind of knowledge that, in principle, should be of use would be that embodied in a thesaurus, classification scheme, or other formalized indexing language. But such knowledge does not seem to fit very easily with established KBS ideas.
My own opinion, for what it's worth, is that the way forward may be to incorporate selective and small-scale "intelligent" (or moderately clever) methods into the associative retrieval framework, without attempting to go all the way to an intelligent system. Cleverness need not take the form expected in the current KBS tradition: a relevance feedback system based on the probabilistic model already seems quite clever to the user. Perhaps the central point is that we are attempting to provide tools to help the user solve his or her own problems; we are not attempting to solve their problems for them. Relatively simple tools may be best suited to that purpose.