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dc.contributor.advisorGetoor, Lise Cen_US
dc.contributor.authorPujara, Jayen_US
dc.date.accessioned2016-06-22T05:48:56Z
dc.date.available2016-06-22T05:48:56Z
dc.date.issued2016en_US
dc.identifierdoi:10.13016/M2420K
dc.identifier.urihttp://hdl.handle.net/1903/18221
dc.description.abstractIn the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.en_US
dc.language.isoenen_US
dc.titleProbabilistic Models for Scalable Knowledge Graph Constructionen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledEpistemologyen_US
dc.subject.pquncontrolledknowledge baseen_US
dc.subject.pquncontrolledknowledge graphen_US
dc.subject.pquncontrolledmarkov random fielden_US
dc.subject.pquncontrolledonline inferenceen_US
dc.subject.pquncontrolledstatistical relational learningen_US


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