Schema matching using machine learning
Michael Stonebraker started a company that claims to do just that, schema matching using machine learning:

https://www.tamr.com/

Their site no longer has many details on their approach but this article talks about some of the techniques they used, like :


  
  Perform fuzzy string comparisons over attribute names using trigram cosine    similarity. 
  Treats a column of data as a document and tokenize its    values with a standard full text parser. Then, measure TF-IDF cosine
  similarity between columns. This method is suitable for text fields.
  Use minimum description length (MDL) to compare values of two    attributes. 
  Compute the ratio of the size of the intersection of two    columns' data to the size of their union. This method is well suited
  for categorical fields with small number of values. 
  Compute Welch's t-test for a pair of columns that contain numeric values and get the probability the columns were drawn from the same
  distribution.