Semantic Cloud Architecture An Integration of Cloud and Semantic Web

2015 ◽  
Vol 8 (2) ◽  
Author(s):  
Jayalakshmi Srinivasan
Keyword(s):  
Cloud Computing ◽  
Linked Data ◽  
Collaborative Work ◽  
Data Sets ◽  
It Services ◽  
Cloud Architecture ◽  
Description Framework ◽  
Data Portability ◽  
Resource Description ◽  
The Web

In the last few years, the amount of structured data made available on the Web in semantic formats has grown by several orders of magnitude. On one side, the Linked Data effort has made available online hundreds of millions of entity descriptions based on the Resource Description Framework (RDF) in data sets. On the other hand, the Web 2.0 community has increasingly embraced the idea of data portability, and the first efforts have already produced billions of RDF equivalent triples either embedded inside HTML pages using micro formats or exposed directly using eRDF (embedded RDF) and RDFa (RDF attributes). In another side Cloud Computing is offering utility concerned IT services to users worldwide. It enables hosting of applications from consumers, scientific and business domains. The beauty of cloud computing is its simplicity. This paper focuses on the process of transitioning from IT architectures of today to Semantic Cloud Architecture. The emphasis is on collaborative work of business and enterprise architects to reduce operational costs and to achieve heights.

scholarly journals Transitioning from XML to RDF: Considerations for an effective move towards Linked Data and the Semantic Web

2016 ◽  
Vol 35 (1) ◽  
pp. 51 ◽  
Author(s):  
Juliet L. Hardesty
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Academic Library ◽  
Data Sets ◽  
Markup Language ◽  
Digital Repository ◽  
Extensible Markup ◽  
Description Framework ◽  
Meaningful Relationships ◽  
Resource Description

Metadata, particularly within the academic library setting, is often expressed in eXtensible Markup Language (XML) and managed with XML tools, technologies, and workflows. Managing a library’s metadata currently takes on a greater level of complexity as libraries are increasingly adopting the Resource Description Framework (RDF). Semantic Web initiatives are surfacing in the library context with experiments in publishing metadata as Linked Data sets and also with development efforts such as BIBFRAME and the Fedora 4 Digital Repository incorporating RDF. Use cases show that transitions into RDF are occurring in both XML standards and in libraries with metadata encoded in XML. It is vital to understand that transitioning from XML to RDF requires a shift in perspective from replicating structures in XML to defining meaningful relationships in RDF. Establishing coordination and communication among these efforts will help as more libraries move to use RDF, produce Linked Data, and approach the Semantic Web.

Linked Data

2019 ◽  
pp. 87-113
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Linked Data ◽  
Query Language ◽  
Exciting Field ◽  
Data Resource ◽  
Simple Protocol ◽  
Web Of Data ◽  
Promising Solution ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The data on the web is heterogeneous and distributed, which makes its integration a sine qua non-condition for its effective exploitation within the context of the semantic web or the so-called web of data. A promising solution for web data integration is the linked data initiative, which is based on four principles that aim to standardize the publication of structured data on the web. The objective of this chapter is to provide an overview of the essential aspects of this fairly recent and exciting field, including the model of linked data: resource description framework (RDF), its query language: simple protocol, and the RDF query language (SPARQL), the available means of publication and consumption of linked data, and the existing applications and the issues not yet addressed in research.

Measuring vocabulary use in the Linked Data Cloud

2017 ◽  
Vol 41 (2) ◽  
pp. 252-271 ◽  
Author(s):  
Alberto Nogales ◽  
Miguel Angel Sicilia-Urban ◽  
Elena García-Barriocanal
Keyword(s):  
Linked Data ◽  
Value Added ◽  
Data Set ◽  
Content Type ◽  
Simple Knowledge Organization System ◽  
Knowledge Organization System ◽  
Web Of Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

Purpose This paper reports on a quantitative study of data gathered from the Linked Open Vocabularies (LOV) catalogue, including the use of network analysis and metrics. The purpose of this paper is to gain insights into the structure of LOV and the use of vocabularies in the Web of Data. It is important to note that not all the vocabularies in it are registered in LOV. Given the de-centralised and collaborative nature of the use and adoption of these vocabularies, the results of the study can be used to identify emergent important vocabularies that are shaping the Web of Data. Design/methodology/approach The methodology is based on an analytical approach to a data set that captures a complete snapshot of the LOV catalogue dated April 2014. An initial analysis of the data is presented in order to obtain insights into the characteristics of the vocabularies found in LOV. This is followed by an analysis of the use of Vocabulary of a Friend properties that describe relations among vocabularies. Finally, the study is complemented with an analysis of the usage of the different vocabularies, and concludes by proposing a number of metrics. Findings The most relevant insight is that unsurprisingly the vocabularies with more presence are those used to model Semantic Web data, such as Resource Description Framework, RDF Schema and OWL, as well as broadly used standards as Simple Knowledge Organization System, DCTERMS and DCE. It was also discovered that the most used language is English and the vocabularies are not considered to be highly specialised in a field. Also, there is not a dominant scope of the vocabularies. Regarding the structural analysis, it is concluded that LOV is a heterogeneous network. Originality/value The paper provides an empirical analysis of the structure of LOV and the relations between its vocabularies, together with some metrics that may be of help to determine the important vocabularies from a practical perspective. The results are of interest for a better understanding of the evolution and dynamics of the Web of Data, and for applications that attempt to retrieve data in the Linked Data Cloud. These applications can benefit from the insights into the important vocabularies to be supported and the value added when mapping between and using the vocabularies.

scholarly journals Improving the Quality of Linked Data Using Statistical Distributions

2018 ◽  
pp. 1638-1664 ◽  
Author(s):  
Heiko Paulheim ◽  
Christian Bizer
Keyword(s):  
Knowledge Base ◽  
Linked Data ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sources ◽  
Data Sets ◽  
Statistical Distributions ◽  
The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Towards Massive RDF Storage in NoSQL Databases

2019 ◽  
pp. 263-284 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

A Review of RDF Storage in NoSQL Databases

2016 ◽  
pp. 210-229 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The Resource Description Framework (RDF) is a model for representing information resources on the Web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. So RDF data management is of increasing importance, and has attracted attentions in the database community as well as the Semantic Web community. Currently much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (“not only SQL”) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

Linked Open Statistical Metadata

2017 ◽  
pp. 297-320
Author(s):  
Franck Cotton ◽  
Daniel Gillman
Keyword(s):  
Semantic Web ◽  
Resource Description Framework ◽  
Statistical Data ◽  
Open Data ◽  
Statistical Analyses ◽  
Statistical Literacy ◽  
Statistical Process ◽  
Description Framework ◽  
Resource Description ◽  
The Web

Linked Open Statistical Metadata (LOSM) is Linked Open Data (LOD) applied to statistical metadata. LOD is a model for identifying, structuring, interlinking, and querying data published directly on the web. It builds on the standards of the semantic web defined by the W3C. LOD uses the Resource Description Framework (RDF), a simple data model expressing content as predicates linking resources between them or with literal properties. The simplicity of the model makes it able to represent any data, including metadata. We define statistical data as data produced through some statistical process or intended for statistical analyses, and statistical metadata as metadata describing statistical data. LOSM promotes discovery and the meaning and structure of statistical data in an automated way. Consequently, it helps with understanding and interpreting data and preventing inadequate or flawed visualizations for statistical data. This enhances statistical literacy and efforts at visualizing statistics.

Technique for Transformation of Data From RDB to XML Then to RDF

2017 ◽  
pp. 70-91 ◽  
Author(s):  
Kaleem Razzaq Malik ◽  
Tauqir Ahmad
Keyword(s):  
Semantic Web ◽  
Relational Database ◽  
Resource Description Framework ◽  
Data Science ◽  
True Nature ◽  
Extensible Markup ◽  
Description Framework ◽  
Mapping Techniques ◽  
Resource Description ◽  
The Web

This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.

scholarly journals RDF 1.1: Knowledge Representation and Data Integration Language for the Web

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 84 ◽  
Author(s):  
Dominik Tomaszuk ◽  
David Hyland-Wood
Keyword(s):  
Knowledge Representation ◽  
Resource Description Framework ◽  
Relational Databases ◽  
Formal Definition ◽  
Natural Languages ◽  
Description Framework ◽  
Generalized Knowledge ◽  
Definition Of ◽  
Resource Description ◽  
The Web

Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.

Sign in / Sign up

Export Citation Format

Share Document