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Data & Knowledge Engineering
A classification of semantic conflicts in heterogeneous database systems
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The VLDB Journal — The International Journal on Very Large Data Bases
Representing and reasoning about semantic conflicts in heterogeneous information systems
Representing and reasoning about semantic conflicts in heterogeneous information systems
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Information integration using contextual knowledge and ontology merging
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Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many "data quality" problems are actually "data misinterpretation" problems--that is, problems caused by heterogeneous data semantics. In this paper, we first identify semantic heterogeneities that, when not resolved, often cause data quality problems. We discuss the especially challenging problem of aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggregated. Then we illustrate how COntext INterchange (COIN) technology can be used to capture data semantics and reconcile semantic heterogeneities, thereby improving data quality.