Graphical models for structure extraction and information integration Sunita Sarawagi, KR School of Information technology, and IIT Bombay India Return to Keynotes Abstract Recent advances in supervised learning over multiple inter-dependent variables have paved the way for accurate and automated methods for information extraction and integration. We present various graphical models for extraction, starting from traditional chain models for plain text, to segmentation models for exploiting matches with existing entities, and general graph models for extracting from visual 2D layouts as in web pages. Such models are trained either via conditional likelihood maximization or margin maximization leading to constrained convex optimization problems. Inferencing often involves more than a simple message passing algorithm because of the presence of constraints that are not captured in the dependency graph. We present algorithms for such constrained inferencing and optimization tricks for reducing the computation of expensive features, like matches with large external dictionaries. There is much scope for further research in handling diverse unstructured sources, continuous model refinement, efficient training and inferencing, and, probabilistic query answering in the presence of source uncertainties. ©2006 Association for Computing Machinery