Retrieval and Ranking of Semantic Entities for Enterprise Knowledge Management Tasks

author: Chad Cumby, Accenture Technology Labs
author: Katharina Probst, Google, Inc.
author: Rayid Ghani, Center for Data Science and Public Policy, University of Chicago
published: May 27, 2009,   recorded: April 2009,   views: 3164

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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 named-entity 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.

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