Discovering Patterns for Fact Checking in Knowledge Graphs

Abstract

This article presents a new framework that incorporates graph patterns to support fact checking in knowledge graphs. Our method discovers discriminant graph patterns to construct classifiers for fact prediction. First, we propose a class of graph fact checking rules (GFCs). A GFC incorporates graph patterns that best distinguish true and false facts of generalized fact statements. We provide statistical measures to characterize useful patterns that are both discriminant and diversified. Second, we show that it is feasible to discover GFCs in large graphs with optimality guarantees. We develop an algorithm that performs localized search to generate a stream of graph patterns, and dynamically assemble the best GFCs from multiple GFC sets, where each set ensures quality scores within certain ranges. The algorithm guarantees a (1/2−ϵ) approximation when it (early) terminates. We also develop a space-efficient alternative that dynamically spawns prioritized patterns with best marginal gains to the verified GFCs. It guarantees a (1−1/e) approximation. Both strategies guarantee a bounded time cost independent of the size of the underlying graph. Third, to support fact checking, we develop two classifiers, which make use of top-ranked GFCs as predictive rules or instance-level features of the pattern matches induced by GFCs, respectively. Using real-world data, we experimentally verify the efficiency and the effectiveness of GFC-based techniques for fact checking in knowledge graphs and verify its application in knowledge exploration and news prediction.