Efficient SPARQL-to-SQL with R2RML mappings

Existing SPARQL-to-SQL translation techniques have limitations that reduce their robustness, efficiency and dependability. These limitations include the generation of inefficient or even incorrect SQL queries, lack of formal background, and poor implementations. Moreover, some of these techniques cannot be used over arbitrary DB schemas due to the lack of support for RDB to RDF mapping languages, such as R2RML. In this paper we present a technique (implemented in the -ontop- system) that tackles all these issues. We propose a formal approach for SPARQL-to-SQL translation that (i) generates efficient SQL by combining optimization techniques from the logic programming and SQL optimization fields; (ii) provides a well-defined specification of the SPARQL semantics used in the translation; and (iii) supports R2RML mappings over general relational schemas. We provide extensive benchmarks using the -ontop- system for Ontology Based Data Access (OBDA) and show that by using these techniques -ontop- is able to outperform well known SPARQL-to-SQL systems, as well as commercial triple stores, by several orders of magnitude.     

基于数据库集群的海量 RDF 数据联合查询系统的研究与实现

随着语义网的快速发展,为了实现科学数据的共享,越来越多的科学数据被加工发布为关联数据,进而应用于关联查询和关联发现。针对大规模关联数据的管理,本文通过构建 RDF 数据库集群来存储海量数据,设计了基于 SPARQL 端点的联合查询系统来解决用户跨机器透明查询的问题,分析了存储策略和联合查询系统的查询处理相关技术。实际运行表明,本平台易于集成使用,可以实现大规模 RDF 数据的可扩展性存储和有效查询。     

RDF partitioning for scalable SPARQL query processing

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even totally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically partitioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these proposed approaches have been evaluated by extensive experiments over large RDF data sets.     

基于属性相关性的SPARQL查询优化方法

目前主流的RDF存储系统都是基于关系数据库的,其查询引擎都是将SPARQL转换为SQL,然后由数据库的查询引擎来执行查询.但是,目前的数据库查询优化器对于连接查询的选择度估计都是基于属性独立假设的,这往往导致估计错误而选择了效率低的执行计划,所以属性相关性信息对于SPARQL查询优化器能否找到效率高的执行计划是非常重要的.针对SPARQL转换为SQL后,因连接操作没有优化导致查询效率不高的问题,提出了利用本体信息自动计算属性相关性的方法,从而调整连接操作的选择度估计值,调整连接顺序,提高SPARQL查询中基本图模式的连接查询效率.     

基于自适应模式的SPARQL查询与优化

传统的RDF存储系统直接将三元组存放到含有3列的关系数据库表中.具体查询时,需扫描整张三元组表,并通过连接操作产生最后的结果.虽然存储直观、实现方便,但是由于每个子查询都需要在整个三元组表上进行,查询效率较低.同时,当实例属性比较多时,大量的连接操作也对查询效率造成影响.为了克服这些缺点,在RDF自适应模式存储系统FlexTable系统上,搭建一个SPARQL查询引擎,将SPARQL查询语句映射到SQL语句,同时根据数据字典信息,对转化后的SQL语句进行优化,提高了查询效率.     

Towards meaning-based machine translation: using abstractions from text generation for preserving meaning

One of the primary motivations of text generation is the achievement of a very wide range of linguistic abilities coupled with functional control of that range. This control rests on the appropriate construction of abstract specifications of meaning that can guide the generation process to produce language that is textually, grammatically, and lexically appropriate. Such abstract semantic specifications, when constructed in the right way, preserve much of the meaning required in a translation without unduly constraining syntactic form. This is potentially of great value for machine translation since it opens up the possibility of domain-independent, constrained, meaning-based translation. This paper describes how the upper model of the PENMAN text generation system provides a level of semantic abstraction of this kind. It offers examples of the motivation of broader sets of likely translational equivalents than that possible with transfers at lower-levels of abstraction and sets out types of constraints by which the set of likely translational equivalents may be reduced to high-quality renderings of the source text.     

Ultrawrap: SPARQL execution on relational data

The Semantic Web’s promise of web-wide data integration requires the inclusion of legacy relational databases,¹ i.e. the execution of SPARQL queries on RDF representation of the legacy relational data. We explore a hypothesis: existing commercial relational databases already subsume the algorithms and optimizations needed to support effective SPARQL execution on existing relationally stored data. The experiment is embodied in a system, Ultrawrap, that encodes a logical representation of the database as an RDF graph using SQL views and a simple syntactic translation of SPARQL queries to SQL queries on those views. Thus, in the course of executing a SPARQL query, the SQL optimizer uses the SQL views that represent a mapping of relational data to RDF, and optimizes its execution. In contrast, related research is predicated on incorporating optimizing transforms as part of the SPARQL to SQL translation, and/or executing some of the queries outside the underlying SQL environment.Ultrawrap is evaluated using two existing benchmark suites that derive their RDF data from relational data through a Relational Database to RDF (RDB2RDF) Direct Mapping and repeated for each of the three major relational database management systems. Empirical analysis reveals two existing relational query optimizations that, if applied to the SQL produced from a simple syntactic translations of SPARQL queries (with bound predicate arguments) to SQL, consistently yield query execution time that is comparable to that of SQL queries written directly for the relational representation of the data. The analysis further reveals the two optimizations are not uniquely required to achieve a successful wrapper system. The evidence suggests effective wrappers will be those that are designed to complement the optimizer of the target database.     

嵌套查询的非嵌套化处理研究

嵌套查询是ＳＱＬ查询语言的重要特色，传统的数据为系统处理嵌套查询的方法是ＴＩＳ．ＴＩＳ方法处理效率很低。目前高嵌套查询处理效率的有效方法是非嵌套化处理方法。     

RDF查询语言到SQL语言的转换原理及其实现方法

RDF查询语言的优点是具有语义性,缺点是对于海量信息的存储和查找的效率都很低．而关系数据库对海量信息的存储和查找的效率皆很高,但是其查询语言SQL却缺乏语义信息．为了使信息查询既有RDF的语义性又有关系数据库的高性能,提出将RDF查询语言到SQL语言的转换原理,并在此基础上实现一个对用户透明的、建立在关系数据库之上的RDF查询引擎．其优点是：可以利用关系数据库来存储和查询RDF信息,提高其海量存储和查找效率;对存储在不同的关系数据库中的关系数据,能够利用RDF的查找特性进行异质数据库之间的信息交换及信息融合．     

一种基于语义的RDF近似查询方法

针对返回结果为空或甚少的情况,提出RDF查询松弛和同源词替换相结合的方法:通过RDFS蕴含规则对初始查询进行松弛,选取合适的松弛查询进行同源词替换得到更多的查询结果.为了返回与初始查询在语义上相近的结果,提出面向RDF的语义距离概念,即通过语义距离的计算选取与初始查询在语义上相近的结果.在上述查询策略的基础上,给出基于语义的RDF近似查询处理的算法,通过实验验证了所提方法的可行性,并与现有的RDF查询方法进行了比较.实验结果表明,所提方法在查准率以及查全率方面均具有一定的优越性.     

基于扩展ASP的RDF流处理系统

对传感器产生的语义数据流执行复杂推理的能力, 最近已成为语义网社区中的重要研究领域, 而目前大多数RDF流处理系统是以SPARQL (W3C标准RDF查询语言)为基础实现的, 但这些引擎在捕获复杂的用户需求和处理复杂的推理任务方面存在局限性. 针对此问题, 本文结合并扩展了回答集编程(Answer Set Programing, ASP)技术用于对RDF流进行连续的处理. 为了验证本方法的有效性, 首先以智能家居本体为实验对象, 并分析传感器设备间的共有特性及复杂事件以构建本体库; 然后基于本体库产生实例对象, 并通过中间件产生RDF数据流; 接下来通过扩展ASP, 充分利用其表达和推理能力以减少推理时间, 并设计了RDF 流的窗口划分策略等, 然后根据用户的请求, 选择性地进行静态知识库加载等; 最后通过实验与Sparkwave和Laser进行对比, 证明了该方法在延迟和内存上的性能优势.     

SPARQL查询引擎设计

论文以通用的不依赖于具体RDF平台的SPARQL查询引擎的设计与实现作为研究对象。并从SPARQL语法解析器、引擎系统的优化设计等方面进行了深入的探讨,提出了合理的设计策略和实现方法。     

基于SPARQL的RDF数据节点间关系路径检索

在分析SPARQL标准和基于Jena的开源SPARQL工具ARQ查询引擎源码的基础上,提出了可支持关联查询的扩展SPARQL标准及其设计和实现方案,认真分析了已有的试验成果。     

语义转换--直接指称语义到接续指称语义

指称语义分为直接指称语义和接续指称语义,其中后一种语义描述的难度较大,给出了直接指称语义描述到接续指称语义描述的转换方法,这就使得这种语义转换的自动化成为可能．转换算法揭示了直接指称语义与接续指称语义之间的内在关系,同时也提供了写接续指称语义描述的有效方法．当需要检验同一种语言的直接指称语义描述和接续指称语义描述是否等价时,提供的技术是很有用的。     

RDF 数据查询处理技术综述

随着语义网以及信息抽取技术等研究的发展,Web上涌现出越来越多的RDF数据,海量RDF数据的管理,已经成为学术界和工业界研究的热点之一。从RDF数据集形态及RDF数据组织存储两个维度以及查询表述、查询处理、查询优化等方面,深入地分析和比较了RDF数据查询处理方法,并在此基础上提出了未来研究的方向和挑战。     

分布式环境下大规模资源描述框架数据划分方法综述

随着知识图谱的日益发展和在各个垂直领域的广泛应用,对于资源描述框架（RDF）数据的高效处理需求日益成为现代大数据管理领域中的新课题。RDF是W3C提出的用于描述知识图谱实体以及实体间关系的数据模型。为了有效地应对大规模RDF数据的存储和查询,很多学者考虑在分布式环境中管理RDF数据。RDF数据的分布式存储所面临的关键问题是数据的划分,而划分的结果很大程度上决定了SPARQL的查询性能。从数据划分的角度,主要围绕两类：基于图结构的RDF数据划分方法和基于语义的RDF数据划分方法展开深入阐述。前者包括多粒度层次划分、模板划分和聚类划分,适用于通用领域查询的语义范畴较为宽泛的场景;后者包括哈希划分、垂直划分和模式划分,更加适用于垂直领域查询的语义范畴相对固定的环境。此外,针对几种典型的划分方法进行对比与分析,为未来RDF数据划分方法的研究提供参考。最后,对未来RDF数据划分方法的发展方向进行了归纳总结。