Abstract: An automated blocking technique is used as a first step to find approximate matches in a database. The technique builds a blocking set to be as liberal as possible in retrieving records that match on individual fields or sets of fields while avoiding selection criteria that are predicted to return more than the maximum number of records defining a particular special requirement. The ability to do blocking without extensive manual setup at low cost is highly advantageous especially when using a machine learning based second-stage matching algorithm.

Abstract: A method of training a system from examples achieves high accuracy by finding the optimal weighting of different clues indicating whether two data items such as database records should be matched or linked. The trained system provides three possible outputs when presented with two data items: yes, no or I don't know (human intervention required). A maximum entropy model can be used to determine whether the two records should be linked or matched. Using the trained maximum entropy model, a high probability indicates that the pair should be linked, a low probability indicates that the pair should not be linked, and intermediate probabilities are generally held for human review.

Abstract: A method of training a system from examples achieves high accuracy by finding the optimal weighting of different clues indicating whether two data items such as database records should be matched or linked. The trained system provides three possible outputs when presented with two data items: yes, no or I don't know (human intervention required). A maximum entropy model can be used to determine whether the two records should be linked or matched. Using the trained maximum entropy model, a high probability indicates that the pair should be linked, a low probability indicates that the pair should not be linked, and intermediate probabilities are generally held for human review.