Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs

TL;DR

This paper introduces a discriminative path-based method for fact checking in knowledge graphs, which improves accuracy, interpretability, and scalability over existing models by mining predicate paths to evaluate factual claims.

Contribution

The paper presents a novel discriminative predicate path mining approach that incorporates connectivity, type, and predicate interactions for fact checking in knowledge graphs.

Findings

Significantly outperforms related models in accuracy.

Provides interpretable reasons for fact verification.

Effective on large-scale knowledge graphs from Wikipedia and PubMedDB.

Abstract

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 present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.