An Architecture for Probabilistic Concept-Based Information Retrieval
This work addresses the effort-intensive knowledge acquisition bottleneck for researchers and practitioners in information retrieval, though it appears incremental as it builds on existing concept-based methods.
The paper tackles the knowledge acquisition problem in concept-based information retrieval by proposing an architecture that uses probabilistic networks and partially automates concept knowledge base construction from data, with experiments on Reuters terrorism documents showing feasibility and advantages.
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.