  Knowledge graphs are a versatile framework to encode richly structured data
relationships, but it can be challenging to combine these graphs with
unstructured data. Methods for retrofitting pre-trained entity representations
to the structure of a knowledge graph typically assume that entities are
embedded in a connected space and that relations imply similarity. However,
useful knowledge graphs often contain diverse entities and relations (with
potentially disjoint underlying corpora) which do not accord with these
assumptions. To overcome these limitations, we present Functional Retrofitting,
a framework that generalizes current retrofitting methods by explicitly
modeling pairwise relations. Our framework can directly incorporate a variety
of pairwise penalty functions previously developed for knowledge graph
completion. Further, it allows users to encode, learn, and extract information
about relation semantics. We present both linear and neural instantiations of
the framework. Functional Retrofitting significantly outperforms existing
retrofitting methods on complex knowledge graphs and loses no accuracy on
simpler graphs (in which relations do imply similarity). Finally, we
demonstrate the utility of the framework by predicting new drug--disease
treatment pairs in a large, complex health knowledge graph.
