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SCALABLE CAPTURING, MODELING AND REASONING OVER COMPLEX TYPES OF DATA FOR HIGH LEVEL ANALYSIS APPLICATIONS

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

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.
机译:结构化和非结构化数据的可伸缩高级融合包括:摄取和处理非结构化数据,以生成存储为提取实体的统计模型,然后将其映射到资源描述框架(RDF)三元组的集合,并将语义分析应用于一组结构化数据数据以生成存储为三元组集合的逻辑模型。将推理器应用于这两种模型,生成基础知识和推断知识的扩展知识图,并将其分解为宽表数据库,每行存储一个对应的三元组,而推理机通过将新列添加到RDF三元组转换为关联的三元组。响应于检测到数据库的行之一中已经存在的主题的新谓词,数据库将新谓词存储在为已存在的主题创建的新行的新列中。

著录项

  • 公开/公告号US2019392074A1

    专利类型

  • 公开/公告日2019-12-26

    原文格式PDF

  • 申请/专利权人 LEAPANALYSIS INC.;

    申请/专利号US201816014791

  • 发明设计人 ERIC LITTLE;

    申请日2018-06-21

  • 分类号G06F17/30;G06N99;

  • 国家 US

  • 入库时间 2022-08-21 11:22:17

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