Reasoning about attribute value equivalence in relational data

In relational data, identifying the distinct attribute values that refer to the same real-world entities is an essential task for many data cleaning and mining applications (e.g., duplicate record detection and functional dependency mining). The state-of-the-art approaches for attribute value matching are mainly based on string similarity among attribute values. However, these approaches may not perform well in the cases where the specified string similarity metric is not a reliable indicator for attribute value equivalence. To alleviate such limitations, we propose a new framework for attribute value matching in relational data. Firstly, we propose a novel probabilistic approach to reason about attribute value equivalence by value correlation analysis. We also propose effective methods for probabilistic equivalence reasoning with multiple attributes. Next, we present a unified framework, which incorporates both string similarity measurement and value correlation analysis by evidential reasoning. Finally, we demonstrate the effectiveness of our framework empirically on real-world datasets. Through extensive experiments, we show that our framework outperforms the string-based approaches by considerable margins on matching accuracy and achieves the desired efficiency.