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SCALABLE CAPTURING, MODELING AND REASONING OVER COMPLEX TYPES OF DATA FOR HIGH LEVEL ANALYSIS APPLICATIONS
SCALABLE CAPTURING, MODELING AND REASONING OVER COMPLEX TYPES OF DATA FOR HIGH LEVEL ANALYSIS APPLICATIONS
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机译:用于高级分析应用程序的可扩展捕获,建模和推理复杂数据类型
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摘要
The scalable high-level fusion of structured and unstructured data includes ingesting and processing unstructured data to produce a statistical model stored as extracted entities then mapped to a collection of resource description framework (RDF) triples, and applying a semantic analysis to a set of structured data to produce a logical model stored as a collection of triples. Reasoners are applied to both models generating an extended knowledge graph of both base and inferred knowledge that is decomposed into a wide table database, with each row storing a corresponding triple, and a reasoner converting the RDF triples into associated triples by adding a new column to the database in response to detecting a new predicate for a subject already present in one of the rows of the database so that the new predicate is stored in the new column in a new row created for the subject already present.
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