DOCUMENT RETRIEVAL USING A PROBABILISTIC KNOWLEDGE MODEL

We are interested in enhancing information retrieval methods by incorporating domain knowledge. In this paper, we present a new document retrieval framework that learns a probabilistic knowledge model and exploits this model to improve document retrieval. The knowledge model is represented by a network of associations among concepts defining key domain entities and is extracted from a corpus of documents or from a curated domain knowledge base. This knowledge model is then used to perform concept-related probabilistic inferences using link analysis methods and applied to the task of document retrieval. We evaluate this new framework on two biomedical datasets and show that this novel knowledge-based approach outperforms the state-of-art Lemur/Indri document retrieval method.