Thinking Fast, Thinking Slow! Combining Knowledge …

Thinking fast , Thinking slow ! Combining Knowledge Graphsand Vector of Maryland, BaltimoreCounty1000 Hilltop Cir, Baltimore, Maryland,USA, 21250 Anupam of Maryland, BaltimoreCounty1000 Hilltop Cir, Baltimore, Maryland,USA, 21250 Tim of Maryland, BaltimoreCounty1000 Hilltop Cir, Baltimore, Maryland,USA, 21250 ABSTRACTK nowledge graphs and vector space models are both robust knowl-edge representation techniques with their individual strengths andweaknesses. Vector space models excel at determining similaritybetween concepts, but they are severely constrained when evaluat-ing complex dependency relations and other logic based operationsthat are a forte of Knowledge graphs. In this paper, we proposetheV-KG structurethat helps us unify Knowledge graphs and vec-tor representation of entities, and allows us to develop powerfulinference methods and search capabilities that combine their com-plementary strengths. We analogize this to Thinking fast in vectorspace along with Thinking deeply and slowly by reasoning overthe Knowledge have also created a query processing engine that takes com-plex queries and decomposes them into subqueries optimized to runon the respective Knowledge graph part or the vector part of show that the V-KG structure can process specific queries thatare not efficiently handled by vector spaces or Knowledge also demonstrate and evaluate the V-KG s

Thinking Fast, Thinking Slow! Combining Knowledge Graphs and Vector Spaces. Sudip Mittal smittal1@umbc.edu University of Maryland, Baltimore County 1000 Hilltop Cir, Baltimore, Maryland, USA, 21250 Anupam Joshi joshi@umbc.edu University of Maryland, Baltimore County 1000 Hilltop Cir, Baltimore, Maryland, USA, 21250 Tim Finin finin@umbc.edu

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  Thinking, Knowledge, Fast, Graph, Combining, Slow, Thinking fast, Thinking slow, Combining knowledge graphs, Combining knowledge

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