Element matching across data-oriented XML sources using a multi-strategy clustering model
Data & Knowledge Engineering
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
A strategy for allowing meaningful and comparable scores in approximate matching
Information Systems
A strategy for allowing meaningful and comparable scores in approximate matching
Information Systems
A self-learning framework for services selection
International Journal of Information Technology and Management
Ontology and instance matching
Knowledge-driven multimedia information extraction and ontology evolution
Linkage of compound objects for supporting maintenance of large-scale web sites
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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The need to leverage the information contained in heterogeneous data sources has been widely documented in recent years. In order to accomplish this goal, an organization must resolve several types of heterogeneity problems that may exist across different data sources. We investigate one such problem called the entity heterogeneity problem. This problem arises when the same real-world entity type is represented using different identifiers in different applications. We propose a decision-theoretic model to resolve the problem. Our model uses a distance-based measure to express the similarity between two entity instances. We have implemented the model, and our experimental results indicate that this is a viable approach in real-world situations.