Semantic Web, RDF, and Portals

2011 ◽  
pp. 905-911
Author(s):  
Ah Lian Kor
Keyword(s):  
Semantic Web ◽  
Web Portals ◽  
Next Generation ◽  
Web Portal ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Resources ◽  
The Subject ◽  
Semantically Enhanced ◽  
Intensive Reading

In existing literature, Semantic Web portals (SWPs) are sometimes known as semantic portals or semantically enhanced portals. It is the next generation Web portal which publishes contents and information readable both by machines and humans. A SWP has all the generic functionalities of a Web portal but is developed using semantic Web technologies. However, it has several enhanced capabilities such as semantics- based search, browse, navigation, automation processes, extraction, and integration of information (Lausen, Stollberg, Hernandez, Ding, Han & Fensel, 2004; Perry & Stiles, 2004). To date the only available resources on SWPs are isolated published Web resources and research or working papers. There is a need to pool these resources together in a coherent way so as to provide the readers a comprehensive idea of what SWPs are, and how they could be built, and these will be supported by some appropriate examples. Additionally, this article will provide useful Web links for more extensive as well as intensive reading on the subject.

Ontology, Web Services, and Semantic Web Portals

2011 ◽  
pp. 658-668
Author(s):  
Ah Lian Kor
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Portals ◽  
Web Portal ◽  
Semantic Web Technologies ◽  
Semantic Query ◽  
Web Technologies ◽  
Ontology Languages ◽  
Rdf Schema

In the article, entitled “Semantic Web, RDF, and Portals”, it is mentioned that a Semantic Web Portal (SWP) has the generic features of a Web portal but is built on semantic Web technologies. This article provides an introduction to two types of Web ontology languages (RDF Schema and OWL), semantic query, Web services, and the architecture of a Semantic Web Portal.

scholarly journals Architecture of the Organic.Edunet Web Portal

2011 ◽  
pp. 759-773
Author(s):  
Nikos Manouselis ◽  
Kostas Kastrantas ◽  
Salvador Sanchez-Alonso ◽  
Jesús Cáceres ◽  
Hannes Ebner
Keyword(s):  
Semantic Web ◽  
Large Scale ◽  
Learning Resources ◽  
Web Portals ◽  
Web Portal ◽  
Semantic Web Technologies ◽  
Web Technologies

The use of Semantic Web technologies in educational Web portals has been reported to facilitate users’ search, access, and retrieval of learning resources. To achieve this, a number of different architectural components and services need to be harmonically combined and implemented. This article presents how this issue is dealt with in the context of a large-scale case study. More specifically, it describes the architecture behind the Organic.Edunet Web portal that aims to provide access to a federation of repositories with learning resources on agricultural topics. The various components of the architecture are presented and the supporting technologies are explained. In addition, the article focuses on how Semantic Web technologies are being adopted, specialized, and put in practice in order to facilitate ontology-aided sharing and reusing of learning resources.

scholarly journals Architecture of the Organic.Edunet Web Portal

2011 ◽  
pp. 54-69
Author(s):  
Nikos Manouselis ◽  
Kostas Kastrantas ◽  
Salvador Sanchez-Alonso ◽  
Jesús Cáceres ◽  
Hannes Ebner ◽  
...  
Keyword(s):  
Semantic Web ◽  
Large Scale ◽  
Learning Resources ◽  
Web Portals ◽  
Web Portal ◽  
Semantic Web Technologies ◽  
Web Technologies

The use of Semantic Web technologies in educational Web portals has been reported to facilitate users’ search, access, and retrieval of learning resources. To achieve this, a number of different architectural components and services need to be harmonically combined and implemented. This article presents how this issue is dealt with in the context of a large-scale case study. More specifically, it describes the architecture behind the Organic.Edunet Web portal that aims to provide access to a federation of repositories with learning resources on agricultural topics. The various components of the architecture are presented and the supporting technologies are explained. In addition, the article focuses on how Semantic Web technologies are being adopted, specialized, and put in practice in order to facilitate ontology-aided sharing and reusing of learning resources.

Resource Allocation for Cloud Computing

2011 ◽  
pp. 90-112 ◽  
Author(s):  
Jorge Ejarque ◽  
Javier Álvarez ◽  
Raül Sirvent ◽  
Rosa M. Badia
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Semantic Web ◽  
Heterogeneous Computing ◽  
New Paradigm ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Computing Paradigm ◽  
Multi Agent ◽  
Semantically Enhanced

Cloud computing has emerged as a distributed computing paradigm where resources are requested on demand and in a very dynamic fashion and paying only for what you consume. This new paradigm created an ecosystem where several providers offer heterogeneous computing resources to satisfy the customers’ computing demand. So, the allocation and adaptation of this demand to the correct resources is a key issue in this ecosystem, because it can produce a mutual benefit for the customers and providers. However, with the wide variety of customers and providers, this allocation is not an easy task. This chapter presents a toolkit that implements a methodology for improving the resource allocation between different Cloud providers. The Semantically Enhanced Resource Allocator (SERA) toolkit introduces the semantic web and multi-agent technologies for facilitating the interoperability between the users and different resource providers. Semantic web technologies provide the required semantic interoperability between the different providers’ vocabularies; meanwhile a platform of configurable agents provides an adaptable and autonomous way of allocating and managing execution requests and resources according to the customers and providers rules.

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources

2011 ◽  
Vol 17 (2) ◽  
pp. 95-115 ◽  
Author(s):  
Miguel A. Mayer ◽  
Pythagoras Karampiperis ◽  
Antonis Kukurikos ◽  
Vangelis Karkaletsis ◽  
Kostas Stamatakis ◽  
...  
Keyword(s):  
Semantic Web ◽  
Search Engines ◽  
Medical Information ◽  
Main Tool ◽  
Web Content ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Resources ◽  
Health Related ◽  
Machine Readable

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various “trust marks” and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.

scholarly journals Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis

2020 ◽  
Author(s):  
Cleon Pereira Júnior ◽  
Clarivando Francisco Belizário Júnior ◽  
Rafael D. Araújo ◽  
Fabiano A. Dorça
Keyword(s):  
Semantic Web ◽  
Experimental Analysis ◽  
Automatic Generation ◽  
Learning Objects ◽  
Personalized Recommendation ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Resources ◽  
The Web

The emerging need to explore the Web as a learning source allied with the purpose of providing personalized recommendations is a tough task. Considering this scenario, this work presents an approach that combines Semantic Web technologies and bio-inspired algorithms to perform personalized recommendation of Learning Objects (LOs) using local repositories and Web resources. Web resources are retrieved and structured as LOs, which allows for the automatic generation of metadata, minimizing course tutors' work. Experiments were performed to verify which bio-inspired evolutionary algorithm would be most appropriate in this context. Also, discussions regarding the quality of recommendations considering local repositories and Web have been made. Initial experiments evaluating the efficiency of the proposed approach have shown promising results.

Semantic Web Portals

2011 ◽  
pp. 901-904
Author(s):  
Shouhong Wang ◽  
Hai Wang
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
General Framework ◽  
The Internet ◽  
Web Portals ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
The Past ◽  
Early Stages ◽  
Definition Of

Web portals, based on traditional Web technologies developed in the late 1990s, present serious limitations regarding information search, extraction, and portal maintenance (Fensel & Musen, 2001). Semantic Web technologies, explored in the past several years, attempt to overcome these limitations. Semantic Web portals are portals based on Semantic Web technologies. Recently, a few Semantic Web portals in their very early stages can be found on the Internet (Lara, Han, Lausen, Stollberg, Ding, & Fensel, 2004). This article will explain the definition of Semantic Web portals, the unique features of Semantic Web portals, and a general framework of architectures of Semantic Web portals.

Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning
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