Fact checking in heterogeneous information networks
Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and show that a new model of the top discriminative meta paths is able to understand the meaning of some statement and accurately determine its veracity. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using public, million node knowledge graphs extracted from Wikipedia and SemMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative path model is easily interpretable and provides sensible reasons for the final determination.