  High-quality automatic speech recognition (ASR) is essential for virtual
assistants (VAs) to work well. However, ASR often performs poorly on VA
requests containing named entities. In this work, we start from the observation
that many ASR errors on named entities are inconsistent with real-world
knowledge. We extend previous discriminative n-gram language modeling
approaches to incorporate real-world knowledge from a Knowledge Graph (KG),
using features that capture entity type-entity and entity-entity relationships.
We apply our model through an efficient lattice rescoring process, achieving
relative sentence error rate reductions of more than 25% on some synthesized
test sets covering less popular entities, with minimal degradation on a
uniformly sampled VA test set.
