Microsoft patents system to match customer data fields to industry standards

1. A system, comprising: a processor; a memory storing instructions that are executable by the processor, the instructions upon execution by the processor cause the processor to: receive a customer schema; identify an industry-specific schema corresponding to the received customer schema; select a first attribute included within the received customer schema for labeling; generate a candidate pair based on the selected first attribute, the candidate pair including the first attribute and a second attribute; generate, using a linguistic featurizer, an embedding featurizer, and a syntactic featurizer for the candidate pair, a featurized candidate based on an identified similarity between the first attribute and the second attribute, the featurized candidate being a 3-tuple numerical vector corresponding to the candidate pair based on the identified similarity including a linguistic similarity, an embedded similarity, and a syntactic similarity between the first attribute and the second attribute in the candidate pair, wherein the linguistic similarity, the embedded similarity, and the syntactic similarity in the 3-tuple numerical vector are identified using the linguistic featurizer, the embedding featurizer, and the syntactic featurizer respectively; generate, using a semi-supervised machine learning (ML) model, a suggested label for the first attribute based on the featurized candidate, the suggested label corresponding to the second attribute, wherein the semi-supervised ML model is trained on labeled training data including the featurized candidate and used to generate pseudo-labels for unlabeled data points, and wherein the semi-supervised ML model is iteratively retrained based on received feedback on the generated pseudo-labels in a train-predict loop until all candidate pairs are labeled or a maximum number of iterations is reached; and apply the suggested label to the first attribute, wherein applying the suggested label comprises updating the linguistic featurizer, based on the applied suggested label, including adding a sentence representation of the candidate pair and the applied suggested label with a larger sample weight than samples generated using only the industry-specific schema.