Retrieval and Ranking of Semantic Entities for Enterprise Knowledge Management Tasks

We describe a task-sensitive approach to retrieval and ranking of semantic entities, using the domain information available in an enterprise. Our approach utilizes noisy namedentity tagging and document classification, on top of an enterprise search engine, to provide input to a novel ranking metric for each entity retrieved for a task. Retrieval is query-centric, where the user query is the target topic (e.g., a technology needed for a proposal). Named entities are then extracted from the retrieved documents, and ranked according to their similarity to the target topic. We evaluate our approach by comparing to a baseline retrieval and ranking technique that is based on entity occurrence rates, and show encouraging results.