Improving Knowledge Validation using Objectivity and Corroborativeness of Web Resources

In this study, we propose a method to validate knowledge candidates using the Web to minimize the false positive rate in a knowledge base (KB). Our approach assesses the objectivity and corroborativeness of a triple, which is the basic form of knowledge, using diverse Web resources. Compared to the state-of-the-art baseline of the Defacto framework, our approach demonstrates superior false positive rates, enabling more effective filtering of false triples in the construction of a KB.