一种大规模RDF语义数据的分布式存储方案

随着语义Web的发展,越来越多的RDF数据发布到Web上,需要一个可以提供存储和查询功能的数据管理系统来对海量的RDF数据进行管理。针对上述问题,设计并实现了一种大规模RDF语义数据的分布式存储方案。该方案通过RDF数据装载和预处理,可以有效地管理海量的RDF数据,并通过构建索引可以有效地对大规模RDF数据进行查询。工作包括底层的RDF存储方案的设计与实现,数据的预处理与装载。同时,设计了一系列实验来评估和对比不同节点数目的Cassandra集群之间的性能,数据采用的是从DBpedia获得的13 million行RDF的数据集。实验结果显示,方案对大规模RDF语义数据的存储和查询具有性能优势。     

An integration site for Semantic Web metadata

The Semantic Web has motivated many communities to provide data in a machine-readable format. However, the available data has not been utilized to far to the extent possible. The data, which has been created by a large number of people, is dispersed across the Web. Creating the data without central coordination results in RDF of varying quality and makes it obligatory to cleanse the collected data before integration. The SECO system presented in this paper harvests RDF files from the Web and consolidates the different data sets into a coherent representation aligned along an internal schema. SECO provides interfaces for humans to browse and for software agents to query the data repository. In this paper, we describe the characteristics of RDF data available online, the architecture and implementation of the SECO application, and discuss some of the experienced gained while collecting and integrating RDF on the Web.     

语义网站点的发现与排序

随着语义网中RDF数据的大量涌现,语义搜索引擎为用户搜索RDF数据带来了便利.但是,如何自动地发现包含语义网信息资源的站点,并高效地在语义网站点中收集语义网信息资源,一直是语义搜索引擎所面临的问题.首先介绍了语义网站点的链接模型.该模型刻画了语义网站点、语义网信息资源、RDF模型和语义网实体之间的关系.基于该模型讨论了语义网实体的归属问题,并进一步定义了语义网站点的发现规则;另外,从站点链接模型出发,定义了语义网站点依赖图,并给出了对语义网站点进行排序的算法.将相关算法在一个真实的语义搜索引擎中进行了初步测试.实验结果表明,所提出的方法可以有效地发现语义网站点并对站点进行排序.     

Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web

The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database—directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently—in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.     

Growing triples on trees: an XML-RDF hybrid model for annotated documents

Since the beginning of the Semantic Web initiative, significant efforts have been invested in finding efficient ways to publish, store, and query metadata on the Web. RDF and SPARQL have become the standard data model and query language, respectively, to describe resources on the Web. Large amounts of RDF data are now available either as stand-alone datasets or as metadata over semi-structured (typically XML) documents. The ability to apply RDF annotations over XML data emphasizes the need to represent and query data and metadata simultaneously. We propose XR, a novel hybrid data model capturing the structural aspects of XML data and the semantics of RDF, also enabling us to reason about XML data. Our model is general enough to describe pure XML or RDF datasets, as well as RDF-annotated XML data, where any XML node can act as a resource. This data model comes with the XRQ query language that combines features of both XQuery and SPARQL. To demonstrate the feasibility of this hybrid XML-RDF data management setting, and to validate its interest, we have developed an XR platform on top of well-known data management systems for XML and RDF. In particular, the platform features several XRQ query processing algorithms, whose performance is experimentally compared.     

RDF数据的并行处理及性能评价

语义Web作为数据之网不断汇集并组织Web信息,相关应用因此面临着对语义Web所含大规模RDF数据高效访问的挑战.利用并行处理技术提高性能是一种解决之道,其核心是RDF数据的放置策略和并行查询处理.已有工作尚未系统研究RDF数据放置策略的分类与特点,及其对查询处理性能的影响.分析了RDF数据上各类数据放置幕略及其对查询处理性能影响,通过LUBM测试基准结果分析评价了典型的RDF并行处理策略(数据放置策略及相应并行查询处理)的实际性能,为提出更有效的并行处理策略奠定了基础.     

ActiveRDF: Embedding Semantic Web data into object-oriented languages

Semantic Web applications share a large portion of development effort with database-driven Web applications. Existing approaches for development of these database-driven applications cannot be directly applied to Semantic Web data due to differences in the underlying data model. We develop a mapping approach that embeds Semantic Web data into object-oriented languages and thereby enables reuse of existing Web application frameworks.We analyse the relation between the Semantic Web and the Web, and survey the typical data access patterns in Semantic Web applications. We discuss the mismatch between object-oriented programming languages and Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas.We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. We demonstrate the usage of ActiveRDF and its integration with the popular Ruby on Rails framework which enables rapid development of Semantic Web applications.     

A general framework for representing,reasoning and querying with annotated Semantic Web data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the Web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g., temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language – AnQL – that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics.     

Semantic caching and query processing

Semantic caching is very attractive for use in distributed systems due to the reduced network traffic and the improved response time. It is particularly efficient for a mobile computing environment, where the bandwidth of wireless links is a major performance bottleneck. Previous work either does not provide a formal semantic caching model, or lacks efficient query processing strategies. This paper extends the existing research in three ways: formal definitions associated with semantic caching are presented, query processing strategies are investigated and, finally, the performance of the semantic cache model is examined through a detailed simulation study.     

面向资源融合网格的数据语义框架

当前语义web服务成了语义web研究和应用的热点之一,提出了几种语义web服务框架,但是这些架构主要是用于web服务的自动发现、自动调用、自动服务纽合,不适合描述网格环境下的数据语义。本文在目前的语义web服务框架基础上,提出面向资源容融合网格（FuseGrid）的数据语义框架（ODSF）。在ODSF中,数据对象的语义是自描述的;ODSF包括4个顶层元素,分别为元属性、数据库本体、映射以及业务规则,其中元属性、数据库本体以及映射本体表示数据对象的静态语义,而业务规则描述数据对象的动态语义。同时分析了该框架的语义数据访问方法及其关键算法。     

基于RDF4S语义服务描述模型的服务资源搜索框架

语义服务资源搜索是一个满足服务请求者快速、便捷地定位可用语义服务资源的过程。一个高效、精确的基于语义的语义服务资源搜索框架和经过精心设计并实现的搜索工具是语义服务框架中的一个必要组件。提出一种基于RDF4S语义服务描述模型的服务资源搜索框架,详细说明了该搜索框架的结构。搜索框架根据RDF4S语义服务描述模型的QoS、执行、功能和接口四种语义标注元素类型,分别采用不同的搜索匹配策略,可以很大程度上提高搜索的效率和精度。     

Evaluating question answering over linked data

The availability of large amounts of open, distributed, and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities, and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here, we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.     

Triple Pattern Fragments: A low-cost knowledge graph interface for the Web

Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers’ data with the necessary reliability.     

Retrieving and semantically integrating heterogeneous data from the Web

Building Finder uses semantic Web technologies to integrate different data types from various online data sources. The application's use of the RDF and RDF data query language makes it usable by computer agents as well as human users. An agent would send a query, expressed in terms of its preferred ontology (schema), to a system that would then find and integrate the relevant data from multiple sources and return it using the agent's ontology. We discuss about retrieving and semantically integrating heterogeneous data from the Web.     

基于语义Web技术的上下文感知的智能移动服务

语义Web技术应用于上下文感知的智能移动服务，通过构建上下文信息本体，使得移动服务的实体之间可以进行上下文信息共享和语义互操作，并进行上下文信息推理，实现智能服务。本文首先介绍了语义Web及本体技术，其次阐述了语义Web技术应用于上下文感知的移动服务，然后详细分析了智能移动服务中的上下文信息本体构建，包括通用的上下文信息本体、用户概况本体、情境本体以及服务本体等，接着介绍了相关的研究项目，最后进行展望和总结。     

基于关键词的语义网数据查询研究综述

语义网数据的关键词查询是语义网研究的一个重要问题。首先给出语义网数据关键词查询的相关定义。根据研究目标不同,将已有解决方案分为混合型和非混合型的语义网数据关键词查询,后者又分为K-A和K-呀A两种查询方法。调研了上述分类中当前常用的解决方案和研究进展。在此基础上,进一步介绍并比较了8个具有代表性的语义网数据关键词查询工作。最后讨论存在的挑战,并指出未来可能的研究方向。