Improving Data Quality Through Effective Use of Data Semantics

Abstract

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 with data semantics. In this paper, we first illustrate some examples of these problems and then introduce a particular semantic problem that we call “corporate householding.” We stress the importance of “context” to get the appropriate answer for each task. Then we propose an approach to handle these tasks using extensions to the COntext INterchange (COIN) technology for knowledge storage and knowledge processing.