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

由于自动提取过程的不确定性、不完整性和不一致，当前大规模知识库（KB）的查询结果不完整、错误和冲突。该提案的研究目标是扩展最先进的知识库系统，以创建一个概率一阶知识库系统，该系统可以使用规则推断缺失的知识，使用约束修剪冲突的知识，并返回结果元组的置信值。该提案中开发的新系统和算法可以通过声明式查询接口对许多高影响力应用程序中存在的大型不确定图（包括知识库、社交网络和生物网络）进行高级在线数据分析。本提案的研究目标是扩展最先进的知识库系统的数据模型、查询语言、查询处理和优化技术，以支持概率一阶知识库系统。 P.I.将设计概率KB图数据模型；使用附加推理运算符将 SPARQL 扩展到概率图查询语言；发明用于可扩展推理查询的新查询执行和优化技术；并在网络规模的概率知识库图上使用统一的数据并行和图并行系统来实现新的查询处理系统。有关更多信息，请参阅项目网站：http://dsr.cise.ufl.edu/Eureka

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