基于BSP的SPARQL基本图模式查询算法

随着语义网的不断发展,发布在互联网上的资源描述框架(RDF)数据达到百亿级三元组规模,并且呈现几何增长趋势,针对RDF数据的单机SPARQL查询方法已经不再适用。为此,提出一种基于整体同步并行(BSP)模型的SPARQL基本图模式查询算法。根据RDF有向图数据特性及基本图模式定义,将整个查询过程分成匹配和迭代2个阶段,在匹配出所需查询的三元组模式后,通过迭代使部分解逐步逼近完全解,得到最终查询结果。利用HAMA分布式计算框架进行算法实现,实验结果表明,与基于MapReduce的SPARQL查询算法相比,该算法具有较高的查询效率,能为大规模RDF数据的快速SPARQL查询提供支持。     

KGDB:统一模型和语言的知识图谱数据库管理系统

知识图谱是人工智能的重要基石,其目前主要有RDF图和属性图两种数据模型,在这两种数据模型之上有数种查询语言,RDF图上的查询语言为SPARQL,属性图上的查询语言主要为Cypher.十年来,各个社区开发了分别针对RDF图和属性图的不同数据管理方法,不统一的数据模型和查询语言限制了知识图谱的更广应用.KGDB （Knowledge Graph Database）是统一模型和语言的知识图谱数据库管理系统：（1）以关系模型为基础,提出统一的存储方案,支持RDF图和属性图的高效存储,满足知识图谱数据存储和查询负载的需求;（2）使用基于特征集的聚类方法解决无类型三元组的存储问题;（3）实现了SPARQL和Cypher两种不同知识图谱查询语言的互操作性,使其能够操作同一个知识图谱.在真实数据集和合成数据集上进行的大量实验表明,KGDB与已有知识图谱数据库管理系统相比,不仅能够提供更加高效的存储管理,而且具有更高的查询效率.KGDB平均比gStore和Neo4j节省了30%的存储空间,基本图模式查询上的实验表明,在真实数据集上的查询速度普遍高于gStore和Neo4j,最快可提高2个数量级.     

基于图数据库的RDF数据分布式存储

RDF数据模型具有天然的图结构,因此以图结构存储可以避免RDF逻辑数据模型到物理数据模型的转换。基于图数据库的RDF数据分布式存储方案,重点讨论RDF图数据流分割、图数据库分布式扩展、SPARQL查询语言转CYPHER图形查询语言等。实验对比了基于Neo4j图数据库与基于MySQL关系型数据库的RDF数据存储方案的处理性能,并验证了RDF图数据流分割算法的有效性。     

一种基于数据划分实现分布式SPARQL查询的方法

当海量RDF数据存储在分布式平台上时,数据划分的策略将直接影响海量数据的查询效率。为了提高分布式平台上的海量数据查询效率,提出一种基于分布式平台的有效数据划分方法。该方法根据RDF数据图的特征将数据分布在集群的各个节点上,并在此基础上对SPARQL查询语句进行分解,实现高效的分布式查询。算法在云平台上实现,并在真实的RDF数据集上对算法进行了测试。实验结果证明,与基准方法相比,该算法在查询效率上有很大的提高。     

RDF数据分布式并行语义编码算法

现有的RDF数据分布式并行压缩编码算法均未考虑结合本体文件,导致编码后的RDF数据没有表示任何语义信息,不利于分布式查询或推理。针对这些问题,提出SCOM(Semantic Coding with Ontology on MapReduce)算法在分布式MapReduce下完成RDF数据的语义并行编码。该算法首先结合RDF数据本体,构建类关系和属性关系模型;在三元组项分类与过滤之后,对三元组项进行编码并生成字典表,最终完成RDF数据带有语义信息且具有规律性的编码。此外,SCOM算法能够很容易地将编码后的RDF数据文件恢复为原始文件。实验表明,SCOM算法能够高效地实现大规模数据的分布式并行编码。     

基于关键词的RDF数据图查询模型研究

随着语义网数据的海量涌现,人们更加关注RDF图的数据查询效率,通过关键词匹配直接查询RDF数据图成为一个研究热点。针对关键词查询中普遍存在的结果冗余与偏离等问题,提出了一种基于关键词的RDF数据图查询模型。该模型首先采用提出的基于迭代的图查询算法(ISGR)对所查询关键词进行子图匹配,得到唯一且最大的结果子图集合;然后根据关键词图与结果子图之间的结构信息,利用统计语言模型,给出了一种结果子图排序方法(SimLM)。对比实验表明,提出的查询模型及排序方法在一致性和相关性方面的性能优于传统模型。     

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。     

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。     

gStore: a graph-based SPARQL query engine

We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.     

Enhancing answer completeness of SPARQL queries via crowdsourcing

Linked Open Data initiatives have encouraged the publication of large RDF datasets into the Linking Open Data (LOD) cloud, including DBpedia, YAGO, and Geo-Names. Despite the size of LOD datasets and the development of (semi-)automatic methods to create and link LOD data, these datasets may be still incomplete, negatively affecting thus accuracy of Linked Data processing techniques. We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL queries. Our system, HARE, implements these hybrid query processing techniques. HARE encompasses several features: (1) a completeness model for RDF that exploits the characteristics of RDF in order to estimate the completeness of an RDF dataset; (2) a crowd knowledge base that captures crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries of a SPARQL query that should be executed against the dataset or via crowd computing to enhance query answer completeness; and (4) a microtask manager that exploits the semantics encoded in the dataset RDF properties, to crowdsource SPARQL sub-queries as microtasks and update the crowd knowledge base with the results from the crowd. Effectiveness and efficiency of HARE are empirically studied on a collection of 50 SPARQL queries against the DBpedia dataset. Experimental results clearly show that our solution accurately enhances answer completeness.     

Extending SPARQL with regular expression patterns (for querying RDF)

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for “Path RDF”) which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.     

Semantics preserving SPARQL-to-SQL translation

Most existing RDF stores, which serve as metadata repositories on the Semantic Web, use an RDBMS as a backend to manage RDF data. This motivates us to study the problem of translating SPARQL queries into equivalent SQL queries, which further can be optimized and evaluated by the relational query engine and their results can be returned as SPARQL query solutions. The main contributions of our research are: (i) We formalize a relational algebra based semantics of SPARQL, which bridges the gap between SPARQL and SQL query languages, and prove that our semantics is equivalent to the mapping-based semantics of SPARQL; (ii) Based on this semantics, we propose the first provably semantics preserving SPARQL-to-SQL translation for SPARQL triple patterns, basic graph patterns, optional graph patterns, alternative graph patterns, and value constraints; (iii) Our translation algorithm is generic and can be directly applied to existing RDBMS-based RDF stores; and (iv) We outline a number of simplifications for the SPARQL-to-SQL translation to generate simpler and more efficient SQL queries and extend our defined semantics and translation to support the bag semantics of a SPARQL query solution. The experimental study showed that our proposed generic translation can serve as a good alternative to existing schema dependent translations in terms of efficient query evaluation and/or ensured query result correctness.     

KGDB: Knowledge Graph Database System with Unified Model and Query Language

Knowledge graph is an important cornerstone of artificial intelligence, which currently has two main data models: RDF graphs and property graphs. There are several query languages on these two data models, including SPARQL on RDF graphs and Cypher on property graphs. Over the last decade, various communities have developed different data management methods for RDF graphs and property graphs. Inconsistent data models and query languages hinder the wider application of knowledge graphs. In this paper, we propose a knowledge graphy database (KGDB) system with unified data model and query language. (1) We work out a unified storage scheme based on the relational model that supports the efficient storage of RDF graphs and property graphs, catering to the smooth storage and query of knowledge graph data. (2) The characteristic set-based clustering is used in KGDB for the storage of typeless entities. (3) It realizes the interoperability of SPARQL and Cypher by enabling them to operate on the same knowledge graph. Extensive experiments on real-world datasets and synthetic datasets reveal that KGDB is more efficient than existing knowledge graph database management systems in storage management and query efficiency. KGDB saves 30% of the storage space on average compared with gStore and Neo4j. In addition, KDGB is two orders of magnitude faster than gStore and Neo4j in the query of the real-world datasets, seen from experiments on the query of basic graph pattern matching.     

Processing SPARQL queries over distributed RDF graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a “partial evaluation and assembly” framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system’s performance and scalability.