Ontology-aware partitioning for knowledge graph identification

Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from noisy extractions presents numerous challenges. Knowledge graph identification (KGI) is a technique for knowledge graph construction that jointly reasons about entities, attributes and relations in the presence of uncertain inputs and ontological constraints. Although knowledge graph identification shows promise scaling to knowledge graphs built from millions of extractions, increasingly powerful extraction engines may soon require knowledge graphs built from billions of extractions. One tool for scaling is partitioning extractions to allow reasoning to occur in parallel. We explore approaches which leverage ontological information and distributional information in partitioning. We compare these techniques with hash-based approaches, and show that using a richer partitioning model that incorporates the ontology graph and distribution of extractions provides superior results. Our results demonstrate that partitioning can result in order-of-magnitude speedups without reducing model performance.