A distance-based approach to entity reconciliation in heterogeneous databases

In modern organizations, decision makers must often be able to quickly access information from diverse sources in order to make timely decisions. A critical problem facing many such organizations is the inability to easily reconcile the information contained in heterogeneous data sources. To overcome this limitation, an organization must resolve several types of heterogeneity problems that may exist across different sources. We examine one such problem called the entity heterogeneity problem, which arises when the same real-world entity type is represented using different identifiers in different applications. A decision-theoretic model to resolve the problem is proposed. Our model uses a distance measure to express the similarity between two entity instances. We have implemented the model and tested it on real-world data. The results indicate that the model performs quite well in terms of its ability to predict whether two entity instances should be matched or not. The model is shown to be computationally efficient. It also scales well to large relations from the perspective of the accuracy of prediction. Overall, the test results imply that this is certainly a viable approach in practical situations