title: Match rows between datasets with noisy unstructured data and no unique identifier
body: <p>Given 2 datasets, one of which is extremely unstructured and noisy, and another which is well structured and complete - does there exist a software tool, API or framework that I can use to match rows in the unstructured dataset to those in the structured dataset given that neither set contains any unique identifier?  Possibly with the ability to produce some sort of confidence score that the rows are a match.  </p>
  
  <p>That is basically the question, but there's more info below if interested:</p>
  
  <p>I have 2 datasets. Both consist of user information, containing names, addresses, contact details and other miscellaneous information about a user.</p>
  
  <p>Dataset #1 is unformatted, unordered, undelimited, noisy (missing values, unconventional data, i.e. a phone number could be +XX (XX) XXX XXX, the next row's phone number could be XXXXXXXXXX). With the only convenience afforded being that each user record is at least on its own row.</p>
  
  <p>Dataset #2 is augmented across roughly 80 separate datasets, however each dataset although not uniform to each other, is well formatted and data is well delineated meaning I can likely normalise these into 1 logical dataset - for the purpose of this question, consider this one set.</p>
  
  <p>Every record in Dataset #1 is contained in Dataset #2 - the problem is there is no unique identifiers and Dataset #1 is noisy and unstructured.</p>