Domain-independent data cleaning via analysis of entity-relationship graph

Abstract

In this article, we address the problem of reference disambiguation . Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RelDC) and the traditional techniques is that RelDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality. Our extensive experiments over two real data sets and over synthetic datasets show that analysis of relationships significantly improves quality of the result.