2. The method of claim 1 , comprising: repeating the sampling, the labeling of the sampled entities, the updating of the positive and negative training seed sets, and the updating of the decision tree and the classification rules until a threshold condition is met, the threshold condition comprising one of: a maximum number of iterations and a change in a number of rules in the classification rules between iterations.
1. A method for providing a semi-supervised data integration model for named entity classification from a first repository of entity information in view of an auxiliary repository of classification assistance data, the method comprising: comparing training data to named entity candidates taken from the first repository, thereby forming a positive training seed set in view of identified commonality between the training data and the named entity candidates; in view of the positive training seed set, populating a decision tree; in view of populating the decision tree, creating classification rules for classifying the named entity candidates; sampling a number of entities from the named entity candidates; in view of the classification rules, labeling the sampled entities as positive examples and/or negative examples; in view of the positive examples and the auxiliary repository, updating the positive training seed set to include identified commonality between the positive examples and the auxiliary repository; in view of the negative examples and the auxiliary repository, updating a negative training seed set to include negative examples which lack commonality with the auxiliary repository; and in view of both the updated positive and negative training seed sets, updating the decision tree and the classification rules.