Web information extraction using Markov logic networks

Abstract

In this paper, we consider the problem of extracting structured data from web pages taking into account both the content of individual attributes as well as the structure of pages and sites. We use Markov Logic Networks (MLNs) to capture both content and structural features in a single unified framework, and this enables us to perform more accurate inference. We show that inference in our information extraction scenario reduces to solving an instance of the maximum weight subgraph problem. We develop specialized procedures for solving the maximum subgraph variants that are far more efficient than previously proposed inference methods for MLNs that solve variants of MAX-SAT. Experiments with real-life datasets demonstrate the effectiveness of our approach.