Establishing Identity Equivalence in Multi-Relational Domains

Identity Equivalence or Alias Detection is an important topic in Intelligence Analysis. Often, terrorists will use multiple different identities to avoid detection. We apply machine learning to the task of determining Identity Equivalence. Two challenges exist in this domain. First, data can be spread across multiple tables. Second, we need to limit the number of false positives. We present a two step approach to combat these issues. First, we use Inductive Logic Programming to find a set of rules that are predictive of aliases. In the second step, we treat each learned rule as a random variable in a Bayesian Network. We use the Bayesian Network to assign a probability that two identities are aliases. We evaluate our technique on several data sets and find that layering Bayesian Network over the rules significantly increases the precision of our system.