Improving Data Quality Through Effective Use of Data Semantics (DKE)
Massachusetts Institute of Technology (MIT) - Sloan School of Management
Hongwei (Harry) Zhu
University of Massachusetts Lowell; Massachusetts Institute of Technology (MIT); Old Dominion University
CISL Working Paper No. 2005-08
MIT Sloan Working Paper No. 4558-05
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.
Number of Pages in PDF File: 20
Keywords: Data Quality, Data Semantics, Semantic Heterogeneity, Ontology, Contextworking papers series
Date posted: October 20, 2005
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