基于语义属性特征图的查询扩展方法

传统的查询扩展方法由于忽略了词之间的语义关系,在不规范的短小关键字上补充扩展的词已经无法达到预期目标。Linked Data技术利用资源描述框架(RDF)图模型形成Linked Open Data Cloud,能提供更多语义信息。针对查询扩展忽略语义的问题,提出了一种基于语义属性特征图的查询扩展方法。该方法将语义网与图的思想融合,利用以DBpedia资源为顶点的属性图加以扩展。首先,通过有监督的学习训练出15种语义属性特征的权重,用于表达扩展资源的有用性;然后,在整个DBpedia图上通过标签属性实现查询关键字到DBpedia匹配资源的映射;再根据属性特征广度搜索出邻接点,并将其作为扩展候选词,最后筛选出词相关行分值最高的作为最终扩展词。实验表明,与LOD Keyword Expansion方法相比,基于语义属性特征图的扩展方法召回率达到0.89,平均逆排序(MRR)提高4个百分点,与用户查询更匹配。     

基于本体分割的语义图概要方法

语义图概要的目的是提取语义图的关键信息,形成原数据集的概要模型以解决大规模语义图的理解、查询、应用难题。为提升现有语义图概要方法效率,提出一种基于本体分割的概要方法。通过本体分割算法对语义图进行分割生成扩展子图。采用形式概念分析对每个扩展子图生成元素的偏序格（又称特征集格）。在此基础上,由所有子图的特征集格形成了原语义图的概要。在关联开放数据集和Berlin SPARQL Benchmark数据集上的实验结果表明,该方法具有较好的可扩展性,有效提高了概要方法的效率。     

应用本体解决面向语义的信息集成中的查询处理

面向语义的信息集成中基于Web的查询处理应用本体来解决在多个领域中检索数据可能产生的语义层的异构,对于在统一的用户界面下提交的查询请求,根据语义,从Web上搜索相关领域的信息,最终将结果显示在用户界面上.该过程是通过查询分解、子查询转换、分发子查询和子查询结果收集及语义转换来完成的.同时,考虑到基于Web的信息查询一般要涉及多个分布的数据源,查询的响应时间要依赖于网络的传输速度,一般来讲比较长,还借鉴了缓存系统的思想,在查询处理架构中引入了缓存数据库,有选择地存放最常用的查询信息,以提高整个查询的速度.     

一种通用的数据可视化模型设计与实现

随着互联网、物联网、云计算等信息技术的迅猛发展,大量非结构化信息不断涌现,对数据可视化与可视化分析的需求不断增加,同时可视化开发成本不断提高。数据可视化模型的研究对于可视化开发必不可少。基于此背景,根据数据信息的分类研究提出一种通用、模块化的数据可视化模型,将可视化流程分为可视化结构映射、可视化空间布局、可视化视图映射三个主要模块,并基于数据可视化模型利用开源的Echarts图表工具包和D3函数库形成可视化工具DVL.js(Data Visualization Layout)。该工具可应用于Web平台,包含丰富的图形展示方式如柱状图、折线图、时序图、饼图、雷达图、层次树、力导向图、标签云等,这些展示方式都提供了完备的数据与交互接口,能应对不同场合的可视化需求。     

基于内容的语义桌面系统设计与实现①

介绍了基于内容的语义桌面系统。系统对桌面数据进行区分,根据桌面内容的特点提供不同的语义查询;采用数据可视化技术展示桌面数据,记录用户的思路;提供数据共享功能,方便用户交换桌面数据;采用插件架构,允许用户方便地扩展功能。     

基于列数据库的RDF数据管理实现

随着语义网的发展,Web上越来越多的开放数据以RDF格式发布,对海量RDF的有效管理是实现语义网的一个重要条件.文中讨论并分析了现有的几种RDF数据存储方法,针对垂直划分的方法,基于列数据库MonetDB,实现了一个RDF数据管理方案.该方案将RDF和RDFS信息分开存储,并在Barton数据集上,设计了包含几种连接的基准查询,对比RDF管理系统Sesame的三元组模式,分别进行了存储空间和查询效率测试.实验结果验证了基于列数据库的垂直划分方案的有效性.     

多点触控技术在三维虚拟博物馆文物数字可视化中的应用

针对传统博物馆文物数据可视化存在交互效果不佳的问题,提出以陕西省历史博物馆为研究对象,构建一个基于多点触控技术的三维虚拟博物馆设计与漫游系统。首先,采用二维形式进行博物馆三维设计;然后通过多点触控屏幕展示博物馆三维设计效果;最后采用基于变分圆形树图实现层次化数据交互可视化,以提升文物数据空间利用率,并采用层次切换交互技术实现文物数据可视化交互。实验结果表明,对于EOL数据的前6层圆布局测试中,对比于传统的圆形树图和加权能量算法,提出的变分圆形树图的空间利用率提升了4%～52%,且将提出多点触控技术应用到三维虚拟博物馆中可实现文物数据的可视化交互。由此说明,提出的算法可有效提升虚拟博物馆的空间利用率,设计的基于多点触控技术的层次化文物数据可视化交互系统具备一定的可行性。     

LinkMF: 结合Linked Data的协同过滤推荐算法

协同过滤(CF)是推荐系统中应用最为广泛的推荐算法之一,然而数据稀疏性和冷启动问题是协同过滤方法的两个主要挑战。由于Linked Data整合了关于实体的丰富且结构化的特征,可以作为额外的信息源来缓解以上两种挑战。该文中我们首次提出了结合Linked Data改进CF推荐算法,基于矩阵分解提出了一种新的CF模型——LinkMF,在保证推荐准确度的基础上利用Linked Data缓解数据稀疏性和冷启动问题。首先,我们从Linked Data中抽取项目的特征表示并为项目建模;然后提出新的相似度度量方法计算项目相似度;最后利用项目相似度约束和指导MF分解过程产生推荐。在MovielLens和YAGO标准数据集上的大量实验结果表明,LinkMF优于现有的一些CF方法,特别在缓解数据稀疏性和冷启动问题上取得很好地效果。     

重排序RDF流三元组模式的蚁群优化方法

RDF是由W3C提出的一种Web数据模型,RDF流是一种遵从链接开放数据（Linked Open Data）、扩展RDF的数据模型,是一种随时间延续无限增长的动态RDF数据集合。在RDF查询处理中,RDF模式是静态编排执行策略的模式,无法满足动态RDF流实时变化的特性。因此,提出一种在RDF流查询处理中重排序RDF流模式的蚁群优化方法。主要研究内容包括：提出了基于AND-OR有向图RDF流代价评估模型;改进了Max-Min蚁群优化算法,并使用改进算法重排序RDF流模式。实验表明,该方法能够高效地动态编排RDF流执行策略。     

一种P2P网络环境下的OLAP查询方案

传统网络环境和P2P环境中,客户端向OLAP服务器提交OLAP查询,并从服务器获取查询结果,OLAP服务器的负载将随着客户端的增加而急剧增加。设计了一种基于P2P（Peer-to-Peer,点对点技术）技术的DQDC（Distributed Query Data Cube,多维数据集的分布式查询）算法,实现P2P网络中语义级的多节点Data Cube数据共享,从而提高系统整体的决策分析性能。     

Querying linked data graphs using semantic relatedness: A vocabulary independent approach

Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, should be able to search and query data spread over potentially large numbers of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipedia-based semantic relatedness measure and spreading activation. Wikipedia-based semantic relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBpedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%.     

Challenges for the multilingual Web of Data

The Web has witnessed an enormous growth in the amount of semantic information published in recent years. This growth has been stimulated to a large extent by the emergence of Linked Data. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in different languages. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages.The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. In this sense, the multilingual Web of Data can be realized in our view as a layer of services and resources on top of the existing Linked Data infrastructure adding (i) linguistic information for data and vocabularies in different languages, (ii) mappings between data with labels in different languages, and (iii) services to dynamically access and traverse Linked Data across different languages.In this article, we present this vision of a multilingual Web of Data. We discuss challenges that need to be addressed to make this vision come true and discuss the role that techniques such as ontology localization, ontology mapping, and cross-lingual ontology-based information access and presentation will play in achieving this. Further, we propose an initial architecture and describe a roadmap that can provide a basis for the implementation of this vision.     

SESLDS: An Extension Scheme for Linked Data Sources Based on Semantically Enhanced Annotation and Reasoning

In the era of Big Data, users prefer to get knowledge rather than pages from Web. Linked Data, a rather new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Besides, as the standard query language for RDF data, SPARQL has become the foundation protocol of Linked Data querying. The core idea of RDF Schema (RDFS) is to extend upon RDF vocabulary and allow attachment of semantics to user defined classes and properties. However, RDFS cannot fully utilize the potential of RDF since it cannot express the implicit semantics between linked entities in Linked Data sources. To fill this gap, in this paper, we design a new semantic annotating and reasoning approach that can extend more implicit semantics from different properties. We firstly establish a well‐defined semantically enhanced annotation strategy for Linked Data sources. In particular, we present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SM_P). We then propose a novel general Semantically Extended Scheme for Linked Data Sources (SES_LDS) to realize the semantic extension over the target Linked Data source through semantically enhanced reasoning. Lastly, based on the experimental analyses, we verify that our proposal has advantages over the initial Linked Data source and can return more valid results.     

A Linked Data approach for the discovery of educational ICT tools in the Web of Data

The use of Information and Communication Technologies (ICT) tools to support learning activities is nowadays generalized. Several educational registries provide information about ICT tools in order to help educators in their discovery and selection. These registries are typically isolated and require much effort to keep tool information up to date. To address this issue, this paper explores whether educational tool registries can be federated to other datasets currently available on the Web of Data. In order to answer this question, and following the Linked Data approach, this paper proposes to collect data from third-party sources, align it to a vocabulary understandable by educators and finally publish it to be consumed by educational applications. This way, an incipient educational dataset can be automatically created and easily maintained, since non-educative information is obtained from updated third-party sources. A case study with practitioners has been carried out to evaluate whether the information about ICT tools provided by this dataset is understandable and useful for educators. Evaluation results show that available information on the Web of Data can be used to obtain suitable tools for real educational settings, thus overcoming the sustainability problems of existing ICT tool registries.     

A systematic literature review of Linked Data‐based recommender systems

Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright © 2015 John Wiley & Sons, Ltd.     

Paggr: Linked Data widgets and dashboards

The Linked Data effort has led to a large number of interconnected and reusable datasets with standardized RDF interfaces. Paggr is a novel Web system that simplifies personalized aggregation and interaction with these structured information sources through SPARQL-driven widgets and dashboards. Paggr utilizes a URI-based drag and drop mechanism for end-users, and a collaborative, browser-based development environment for widget creators.     

语义物联网中链接传感器数据发布系统

针对传感器网络数据的表示和传输标准不统一以及单一数据资源无法满足应用程序的需求问题,提出将传感器网络数据发布为链接传感器数据的方法。在分析已有链接传感器数据发布方法的基础上,首先利用本体标注技术为传感器网络数据添加语义信息,然后提出基于继承关系概念组集的关联数据查询处理方法以找到相关Web数据集(RWD)和基于启发式属性的图相似性比较方法实现传感器网络数据与相关Web数据的链接,最后构建了一种链接传感器数据发布系统(LSDPS)。通过与其他经典链接传感器数据发布系统比较,表明本系统将建立与相关数据的链接的准确率提高了9%。将传感器网络数据发布为链接传感器数据,使得应用程序不仅可以理解和利用传感器网络数据,而且可以根据链接传感器数据集间的资源描述框架(RDF)链接获得更多相关资源。     

R2LD: Schema-based Graph Mapping of relational databases to Linked Open Data for multimedia resources data

Multimedia Tools and Applications - The Web of Data used to share and interchange the diverse data of heterogeneous types on the Web has been actively established. Ontology-based Linked Open Data...     

SPUD—Semantic Processing of Urban Data

We present SPUD, a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for the incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogeneous datasets coming from distributed data sources and spanning multiple domains.