Object Matching for Information Integration: A Profiler-Based Approach

Object matching is a fundamental problem that arises in numerous information integration scenarios. Virtually all existing solutions to this problem have assumed that the objects to be matched share the same set of attributes, and that they can be matched by comparing the similarities of the attributes. We consider the more general problem where the objects can also have disjoint attributes, such as matching tuples that come from relational tables with schemas (age,name) and (name,salary), respectively. We describe PROM, a solution that also exploits the disjoint attributes to improve matching accuracy. In the above example, PROM begins by matching any two given tuples based on the shared attribute name. Then it applies a set of profilers, each of which contains some knowledge about what constitutes a typical person. The profilers examine the tuple pair to see if it can plausibly make up a person. For example, a profiler may state that because the age is 9 and the salary is 200K, the tuples do not make up a person and thus do not match. Profilers can be manually specified by domain experts, learned from training data, transferred from other matching tasks, or constructed from external data. Thus, the PROM approach is distinguished in that it not only can exploit disjoint attributes to improve matching accuracy, but can also reuse knowledge from previous object matching tasks.

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