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Query-time entity resolution (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bhattacharya, Indrajit Getoor, Lise |
| Description | Entity resolution is the problem of reconciling 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 query-time entity resolution: quick and accurate resolution for answering queries over such ‘unclean ’ databases at query-time. Since collective entity resolution approaches — where related references are resolved jointly — have been shown to be more accurate than independent attribute-based resolution for off-line entity resolution, we focus on developing new algorithms for collective resolution for answering entity resolution queries at query-time. For this purpose, we first formally show that, for collective resolution, precision and recall for individual entities follow a geometric progression as neighbors at increasing distances are considered. Unfolding this progression leads naturally to a two stage ‘expand and resolve ’ query processing strategy. In this strategy, we first extract the related records for a query using two novel expansion operators, and then resolve the extracted records collectively. We then show how the same strategy can be adapted for query-time entity resolution by identifying and resolving only those database references that are the most helpful for processing 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. In addition to experiments on real datasets, we use synthetically generated data to empirically demonstrate the validity of the performance trends predicted by our analysis of collective entity resolution over a wide range of structural characteristics in the data. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2006-01-01 |
| Publisher Institution | In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD |
| Access Restriction | Open |
| Subject Keyword | Real-world Entity Adaptive Strategy New Algorithm Database Reference Unclean Database Geometric Progression Large Real-world Publication Database Entity Resolution Query Collective Entity Resolution Approach Off-line Entity Resolution Accurate Resolution Individual Entity Related Record Extracted Record Collective Entity Resolution Adaptive Approach Collective Resolution Novel Expansion Operator Available Database Query-time Entity Resolution Structural Characteristic Wide Range Entity Resolution Query Processing Strategy Real Datasets Query Processing Performance Trend Stage Expand Independent Attribute-based Resolution |
| Content Type | Text |
| Resource Type | Article |
