Addressing Instance Ambiguity in Web Harvesting

Abstract

Web Harvesting enables the enrichment of incomplete data sets by retrieving required information from the Web. However, the ambiguity of instances may greatly decrease the quality of the harvested data, given that any instance in the local data set may become ambiguous when attempting to identify it on the Web. Although plenty of disambiguation methods have been proposed to deal with the ambiguity problems in various settings, none of them are able to handle the instance ambiguity problem in Web Harvesting. In this paper, we propose to do instance disambiguation in Web Harvesting with a novel disambiguation method inspired by the idea of collaborative identity recognition. In particular, we expect to find some common properties in forms of latent shared attribute values among instances in the list, such that these shared attribute values can differentiate instances within the list against those ambiguous ones on the Web. Our extensive experimental evaluation illustrates the utility of collaborative disambiguation for a popular Web Harvesting application, and shows that it substantially improves the accuracy of the harvested data.