The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such p...

• Type: Conference
• Year: 2010
• Where: SDM
• Authors: B. Delaney, Andrew S. Fast, W. M. Campbell, C. J. Weinstein, David D. Jensen
• Keywords: Data Mining, Decision Makers, Manual Analysis, SDM 2010, Statistical Relational Learning