III: Small: Efficient Query Processing over Large Probabilistic Knowledge Bases

III：小型：大型概率知识库的高效查询处理

• 批准号: 1526753
• 负责人: Daisy Zhe Wang
• 金额: $ 49.99万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526753&HistoricalAwards=false
• 关键词: III; Small; Efficient; Query; Processing

Due to the uncertainty, incompleteness and inconsistency from automatic extraction processes, query results from current large-scale knowledge bases (KBs) are incomplete, erroneous and conflicting. The research objective of this proposal is to extend the state-of-the-art KB systems to create a probabilistic first-order KB system that can infer missing knowledge using rules, prune conflicting knowledge using constraints, and return confidence values for resulting tuples. The new system and algorithms developed in this proposal can enable advanced online data analysis through an declarative query interface over large uncertain graphs exist in many high impact applications, including knowledge bases, social networks, and biological networks. The research objective of this proposal is to extend the data model, query language, query processing and optimization techniques of the state-of-the-art KB systems to support a probabilistic first-order KB system. The P.I. will design a probabilistic KB graph data model; extend SPARQL to probabilistic graph query language with additional inference operators; invent new query execution and optimization techniques for scalable inference queries; and implement a new query processing system using a unified data-parallel and graph-parallel system over web-scale probabilistic KB graphs.For further information see the project website at: http://dsr.cise.ufl.edu/Eureka

由于自动提取过程的不确定性，不完整和不一致的不一致，因此当前大规模知识库（KB）的查询结果是不完整的，错误的和冲突的。该提案的研究目标是扩展最新的KB系统，以创建一个概率的一阶KB系统，该系统可以使用规则来推断缺失的知识，修剪使用约束的修剪知识以及为结果元素返回置信值。本提案中开发的新系统和算法可以通过在许多高影响应用程序中，包括知识库，社交网络和生物网络在许多高影响应用程序中通过声明的查询界面来实现高级的在线数据分析。该建议的研究目标是扩展最先进的KB系统的数据模型，查询语言，查询处理和优化技术，以支持概率的一阶KB系统。 P.I.将设计一个概率的KB图数据模型；将SPARQL扩展到具有其他推理操作员的概率图查询语言；为可扩展的推理查询发明新的查询执行和优化技术；并通过Web规模的概率KB图形使用统一数据并行和图形并行系统实现新的查询处理系统。有关更多信息，请参见项目网站：http：//dsr.cise.ufl.edu/eureka/eureka