Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
  Knowledge graphs and vector space models are robust knowledge representation
techniques with individual strengths and weaknesses. Vector space models excel
at determining similarity between concepts, but are severely constrained when
evaluating complex dependency relations and other logic-based operations that
are a strength of knowledge graphs. We describe the VKG structure that helps
unify knowledge graphs and vector representation of entities, and enables
powerful inference methods and search capabilities that combine their
complementary strengths. We analogize this to thinking `fast' in vector space
along with thinking 'slow' and `deeply' by reasoning over the knowledge graph.
We have created a query processing engine that takes complex queries and
decomposes them into subqueries optimized to run on the respective knowledge
graph or vector view of a VKG. We show that the VKG structure can process
specific queries that are not efficiently handled by vector spaces or knowledge
graphs alone. We also demonstrate and evaluate the VKG structure and the query
processing engine by developing a system called Cyber-All-Intel for knowledge
extraction, representation and querying in an end-to-end pipeline grounded in
the cybersecurity informatics domain.
