Leveraging Social Media Signals for Record Linkage

Many data-intensive applications collect (structured) data from a variety of sources. A key task in this process is record linkage, which is the problem of determining the records from these sources that refer to the same real-world entities. Traditional approaches use the record representation of entities to accomplish this task. With the nascence of social media, entities on the Web are now accompanied by user generated content. We present a method for record linkage that uses this hitherto untapped source of entity information. We use document-based distances, with an emphasis on word embedding document distances, to determine if two entities match. Our rationale is that user evaluations of entities converge in semantic content, and hence in the word embedded space, as the number of user evaluations grows. We analyze the effectiveness of the proposed method both as a stand-alone method and in combination with record-based record linkage methods. Experimental results using real-world reviews demonstrate the high effectiveness of our approach. To our knowledge, this is the first work exploring the use of user generated content accompanying entities in the record linkage task.