LAUSR

Given a knowledge graph and a fact (a triple statement), fact checking is to decide whether the fact belongs to the missing part of the graph. Facts in real-world knowledge bases are typically interpreted by both topological and semantic context that is not fully exploited by existing methods. This paper introduces a novel fact checking method that explicitly exploits discriminant subgraph structures. Our method discovers discriminant subgraphs associated with a set of training facts, characterized by a class of graph fact checking rules. These rules incorporate expressive subgraph patterns to jointly describe both topological and ontological constraints. (1) We extend graph fact checking rules ($${\mathsf{GFCs}}$$GFCs) to a class of ontological graph fact checking rules ($${\mathsf{OGFCs}}$$OGFCs). $${\mathsf{OGFCs}}$$OGFCs generalize $${\mathsf{GFCs}}$$GFCs by incorporating both topological constraints and ontological closeness to best distinguish between true and false fact statements. We provide quality measures to characterize useful patterns that are both discriminant and diversified. (2) Despite the increased expressiveness, we show that it is feasible to discover $${\mathsf{OGFCs}}$$OGFCs in large graphs with ontologies, by developing a supervised pattern discovery algorithm. To find useful $${\mathsf{OGFCs}}$$OGFCs as early as possible, it generates subgraph patterns relevant to training facts and dynamically selects patterns from a pattern stream with a small update cost per pattern. We verify that $${\mathsf{OGFCs}}$$OGFCs can be used as rules and provide useful features for other statistical learning-based fact checking models. Using real-world knowledge bases, we experimentally verify the efficiency and the effectiveness of $${\mathsf{OGFC}}$$OGFC-based techniques for fact checking.