Reducing inconsistency in integrating data from different sources

One of the main problems in integrating databases into a common repository is the possible inconsistency of the values stored in them, i.e., the very same term may have different values, due to misspelling, a permuted word order, spelling variants and so on. The authors present an automatic method for reducing inconsistency found in existing databases, and thus, improving data quality. All the values that refer to a same term are clustered by measuring their degree of similarity. The clustered values can be assigned to a common value that, in principle, could be substituted for the original values. We evaluate four different similarity measures for clustering with and without expansion of abbreviations. The method we propose may work well in practice but it is time-consuming. In order to reduce this problem, we remove stop words for speeding up the clustering.