Sciweavers

Share
WWW
2011
ACM

Web information extraction using Markov logic networks

9 years 5 months ago
Web information extraction using Markov logic networks
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. MLNs allow us to model a wide range of rich structural features like proximity, precedence, alignment, and contiguity, using first-order clauses. 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 MLN-based approach compared to existing state-of-the-art extraction methods.
Sandeepkumar Satpal, Sahely Bhadra, Sundararajan S
Added 29 May 2011
Updated 29 May 2011
Type Journal
Year 2011
Where WWW
Authors Sandeepkumar Satpal, Sahely Bhadra, Sundararajan Sellamanickam, Rajeev Rastogi, Prithviraj Sen
Comments (0)
books