  Path queries on a knowledge graph can be used to answer compositional
questions such as "What languages are spoken by people living in Lisbon?".
However, knowledge graphs often have missing facts (edges) which disrupts path
queries. Recent models for knowledge base completion impute missing facts by
embedding knowledge graphs in vector spaces. We show that these models can be
recursively applied to answer path queries, but that they suffer from cascading
errors. This motivates a new "compositional" training objective, which
dramatically improves all models' ability to answer path queries, in some cases
more than doubling accuracy. On a standard knowledge base completion task, we
also demonstrate that compositional training acts as a novel form of structural
regularization, reliably improving performance across all base models (reducing
errors by up to 43%) and achieving new state-of-the-art results.
