Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs

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dc.contributor.advisorUMBC Faculty Collection
dc.contributor.authorMittal, Sudip
dc.contributor.authorJoshi, Anupam
dc.contributor.authorFinin, Tim
dc.date.accessioned2018-09-05T19:48:53Z
dc.date.available2018-09-05T19:48:53Z
dc.description.abstractKnowledge 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.en_US
dc.description.urihttps://arxiv.org/abs/1708.03310en_US
dc.format.extent10 PAGESen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/M28P5VD2M
dc.identifier.uri2017arXiv170803310M
dc.identifier.urihttp://hdl.handle.net/11603/11238
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectComputer Science - Artificial Intelligenceen_US
dc.subjectKnowledge Representationen_US
dc.subjectVector Space Modelsen_US
dc.subjectKnowledge Graphsen_US
dc.subjectCybersecurity Informaticsen_US
dc.subjectUMBC Ebiquity Research Group
dc.titleThinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphsen_US
dc.typeTexten_US

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