AnHai Doan
Alon Y. Halevy
Abstract
Semantic heterogeneity is one of the key challenges in integrating and sharing data across disparate sources, data exchange and migration, data warehousing, model management, the Semantic Web and peer-to-peer databases. Semantic heterogeneity can arise at the schema level and at the data level. At the schema level, sources can differ in relations, attribute and tag names, data normalization, levels of detail, and the coverage of a particular domain. The problem of reconciling schema-level heterogeneity is often referred to as schema matching or schema mapping. At the data level, we find different representations of the same real-world entities (e.g., people, companies, publications, etc.). Reconciling data-level heterogeneity is referred to as data deduplication, record linkage, and entity/object matching. To exacerbate the heterogeneity challenges, schema elements of one source can be represented as data in another. This special issue presents a set of articles that describe recent work on semantic heterogeneity at the schema level.
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Digital Library
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