Record Linkage with Uniqueness Constraints and Erroneous Values

Many data-management applications require integrating data from a variety of sources, where different sources may refer to the same real-world entity in different ways and some may even provide erroneous data. An important task in this process is to recognize and merge the various references that refer to the same entity. In practice, some attributes satisfy a uniqueness constraint—each real-world entity (or most entities) has a unique value for the attribute (e.g., business contact phone, address, and email). Traditional techniques tackle this case by first linking records that are likely to refer to the same real-world entity, and then fusing the linked records and resolving conflicts if any. Such methods can fall short for three reasons: first, erroneous values from sources may prevent correct linking; second, the real world may contain exceptions to the uniqueness constraints and always enforcing uniqueness can miss correct values; third, locally resolving conflicts for linked records may overlook important global evidence. This paper proposes a novel technique to solve this problem. The key component of our solution is to reduce the problem into a k-partite graph clustering problem and consider in clustering both similarity of attribute values and the sources that associate a pair of values in the same record. Thus, we perform global linkage and fusion simultaneously, and can identify incorrect values and differentiate them from alternative representations of the correct value from the beginning. In addition, we extend our algorithm to be tolerant to a few violations of the uniqueness constraints. Experimental results show accuracy and scalability of our technique.