Indrajit Bhattacharya, Lise Getoor
Entity resolution is a critical component of data integration where the goal is to reconcile database references corresponding to the same real-world entities. Given the abundance of publicly available databases that have unresolved entities, we motivate the problem of quick and accurate resolution for answering queries over such `unclean' databases. Since collective entity resolution approaches--where related references are resolved jointly--have been shown to be more accurate than independent attribute-based resolution, we focus on adapting collective resolution for answering queries. We propose a two-stage collective resolution strategy for processing queries. We then show how it can be performed on-the-fly by adaptively extracting and resolving those database references that are the most helpful for resolving the query. We validate our approach on two large real-world publication databases where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing. We then show how the same queries can be answered in real time using our adaptive approach while preserving the gains of collective resolution.
Subjects: 12. Machine Learning and Discovery; 1.10 Information Retrieval
Submitted: Apr 24, 2007