  The recent proliferation of publicly available graph-structured data has
sparked an interest in machine learning algorithms for graph data. Since most
traditional machine learning algorithms assume data to be tabular, embedding
algorithms for mapping graph data to real-valued vector spaces has become an
active area of research. Existing graph embedding approaches are based purely
on structural information and ignore any semantic information from the
underlying domain. In this paper, we demonstrate that semantic information can
play a useful role in computing graph embeddings. Specifically, we present a
framework for devising embedding strategies aware of domain-specific
interpretations of graph nodes and edges, and use knowledge of downstream
machine learning tasks to identify relevant graph substructures. Using two
real-life domains, we show that our framework yields embeddings that are simple
to implement and yet achieve equal or greater accuracy in machine learning
tasks compared to domain independent approaches.
