An Architecture for Probabilistic Concept-Based Information Retrieval
  While concept-based methods for information retrieval can provide improved
performance over more conventional techniques, they require large amounts of
effort to acquire the concepts and their qualitative and quantitative
relationships. This paper discusses an architecture for probabilistic
concept-based information retrieval which addresses the knowledge acquisition
problem. The architecture makes use of the probabilistic networks technology
for representing and reasoning about concepts and includes a knowledge
acquisition component which partially automates the construction of concept
knowledge bases from data. We describe two experiments that apply the
architecture to the task of retrieving documents about terrorism from a set of
documents from the Reuters news service. The experiments provide positive
evidence that the architecture design is feasible and that there are advantages
to concept-based methods.
