  Due to the ubiquitous use of embeddings as input representations for a wide
range of natural language tasks, imputation of embeddings for rare and unseen
words is a critical problem in language processing. Embedding imputation
involves learning representations for rare or unseen words during the training
of an embedding model, often in a post-hoc manner. In this paper, we propose an
approach for embedding imputation which uses grounded information in the form
of a knowledge graph. This is in contrast to existing approaches which
typically make use of vector space properties or subword information. We
propose an online method to construct a graph from grounded information and
design an algorithm to map from the resulting graphical structure to the space
of the pre-trained embeddings. Finally, we evaluate our approach on a range of
rare and unseen word tasks across various domains and show that our model can
learn better representations. For example, on the Card-660 task our method
improves Pearson's and Spearman's correlation coefficients upon the
state-of-the-art by 11% and 17.8% respectively using GloVe embeddings.
