Fixing Weakly Annotated Web Data Using Relational Models

In this paper, we present a fast and scalable Bayesian model for improving weakly annotated data – which is typically generated by a (semi) automated information extraction (IE) system from Web documents. Weakly annotated data suffers from two major problems: they (i) might contain incorrect ontological role assignments, and (ii) might have many missing attributes. Our experimental evaluations with the TAP and RoadRunner data sets, and a collection of 20,000 home pages from university, shopping and sports Web sites, indicate that the model described here can improve the accuracy of role assignments from 40% to 85% for template driven sites, from 68% to 87% for non-template driven sites. The Bayesian model is also shown to be useful for improving the performance of IE systems by informing them with additional domain information.

• Type: Conference
• Year: 2007
• Where: ICWE
• Authors: Fatih Gelgi, Srinivas Vadrevu, Hasan Davulcu
• Keywords: Bayesian Model, ICWE 2007, Role Assignments, Scalable Bayesian Model