  We study the problem of entity-relation extraction in the presence of
symbolic domain knowledge. Such knowledge takes the form of an ontology
defining relations and their permissible arguments. Previous approaches set out
to integrate such knowledge in their learning approaches either through
self-training, or through approximations that lose the precise meaning of the
logical expressions. By contrast, our approach employs semantic loss which
captures the precise meaning of a logical sentence through maintaining a
probability distribution over all possible states, and guiding the model to
solutions which minimize any constraint violations. With a focus on low-data
regimes, we show that semantic loss outperforms the baselines by a wide margin.
