Thinking Fast, Thinking Slow! Combining Knowledge Graphs and Vector Spaces
Published 2017-08-10Version 1
Knowledge graphs and vector space models are both robust knowledge representation techniques with their individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but they are severely constrained when evaluating complex dependency relations and other logic based operations that are a forte of knowledge graphs. In this paper, we propose the V-KG structure that helps us unify knowledge graphs and vector representation of entities, and allows us to develop powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking `deeply' and `slowly' by reasoning over the knowledge graph. We have also created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph part or the vector part of V-KG. We show that the V-KG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the V-KG 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.