8. A computer program product 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 computer program product comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code being executable by a computer to perform a 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.
8. A computer program product 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 computer program product comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code being executable by a computer to perform a 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. 11. The computer program product of claim 8 , comprising: removing aliases from the first repository to determine the named entity candidates; eliminating common stop words and non-content-bearing words from candidate entity content of the named entity candidates; populating a feature dictionary in view of high frequency words in the candidate entity content of the named entity candidates; representing the candidate entity content as a vector space model by applying weights to each word of the feature dictionary in the candidate entity content; and preprocessing the auxiliary repository to remove false positive examples.