Exploiting context analysis for combining multiple entity resolution systems

Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The framework proposed in this paper leverages the observation that often no single ER method always performs the best, consistently outperforming other ER techniques in terms of quality. Instead, different ER solutions perform better in different contexts. The framework employs two novel combining approaches, which are based on supervised learning. The two approaches learn a mapping of the clustering decisions of the base-level ER sy...

• Type: Conference
• Year: 2009
• Where: SIGMOD
• Authors: Zhaoqi Chen, Dmitri V. Kalashnikov, Sharad Mehrotra
• Keywords: Base-level Er Systems, Database, ER Ensemble Framework, SIGMOD 2009, Single Er Method