scholarly journals A Review on Semantic web based E-learning Applications

2017 ◽  
Vol 19 (04) ◽  
pp. 34-38
Author(s):  
Vinay M ◽  
Dr. Deepaanand
Keyword(s):  
Semantic Web ◽  
Web Based ◽  
E Learning

Conclusions

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Large Scale ◽  
Web Search ◽  
Web Based ◽  
E Learning ◽  
Computer Based ◽  
Intelligent Devices ◽  
Automated Data Integration ◽  
The Web

At the start of this book we outlined the challenges of automatic computer based processing of information on the Web. These numerous challenges are generally referred to as the ‘vision’ of the Semantic Web. From the outset, we have attempted to take a realistic and pragmatic view of this vision. Our opinion is that the vision may never be fully realized, but that it is a useful goal on which to focus. Each step towards the vision has provided new insights on classical problems in knowledge representation, MASs, and Web-based techniques. Thus, we are presently in a significantly better position as a result of these efforts. It is sometimes difficult to see the purpose of the Semantic Web vision behind all of the different technologies and acronyms. However, the fundamental purpose of the Semantic Web is essentially large scale and automated data integration. The Semantic Web is not just about providing a more intelligent kind of Web search, but also about taking the results of these searches and combining them in interesting and useful ways. As stated in Chapter 1, the possible applications for the Semantic Web include: automated data mining, e-science experiments, e-learning systems, personalized newspapers and journals, and intelligent devices. The current state of progress towards the Semantic Web vision is summarized in Figure 8.1. This figure shows a pyramid with the human-centric Web at the bottom, sometimes termed the Syntactic Web, and the envisioned Semantic Web at the top. Throughout this book, we have been moving upwards on this pyramid, and it should be clear that a great deal of progress that has been made towards the goal. This progress is indicated by the various stages of the pyramid, which can be summarized as follows: • The lowest stage on the pyramid is the basic Web that should be familiar to everyone. This Web of information is human-centric and contains very little automation. Nonetheless, the Web provides the basic protocols and technologies on which the Semantic Web is founded. Furthermore, the information which is represented on the Web will ultimately be the source of knowledge for the Semantic Web.

scholarly journals Implementing E-Learning Ontology to Scale For Provenance

2019 ◽  
Vol 8 (9) ◽  
pp. 670-675
Keyword(s):  
Semantic Web ◽  
Learning Model ◽  
Model Data ◽  
Web Based ◽  
Semantic Data ◽  
Web Based Learning ◽  
Proposed Model ◽  
E Learning ◽  
Academic Resource ◽  
Future Work

This paper explores the aspects of providing education through E-learning model evaluating its relevance to distance education and for ICT systems. A subset of E-learning is a Web based learning that makes the learning -easier, impressive, structured and properly managed. The paper defines an university ontology describing how e-learning provides resources which are available online and designated cloud that can be delivered anywhere any time among the users. In the proposed model data is stored in designated cloud and users are able to share efficiently the same as it provides services to learner. Provenance or trust with respect to the academic resource is a major concern in these types of models, users accessing data must be trustable which help learners, researchers, developers, and users in future work also. This paper proposes an e-learning model which is well organized and structured, such that the machine responds with the accurate, trustable, desired information and results. The paper defines an ontology for semantic structuring, semantic rendering and applies provenance on suggested ontology to achieve authentic results. It is also desired to establish trust of the source contents of the Semantic Web, with the result that a user receiving data will need to verify whether the received data from source is in fact trustable or not. The defined ontogoly is suitable for consumption of both man and machine in the context of the e-learning and Semantic data rendering Web Keywords

scholarly journals Semantic web based e-learning system for sports domain

2010 ◽  
Vol 8 (14) ◽  
pp. 21-25 ◽  
Author(s):  
S.Muthu lakshmi ◽  
G.V. Uma
Keyword(s):  
Semantic Web ◽  
Learning System ◽  
Web Based ◽  
E Learning

E-Learning and Semantic Technologies

2011 ◽  
pp. 810-816
Author(s):  
Konstantinos Markellos ◽  
Penelope Markellou
Keyword(s):  
Semantic Web ◽  
Information And Communication Technologies ◽  
Teaching And Learning ◽  
Modern Society ◽  
Communication Technologies ◽  
Semantic Web Technologies ◽  
Web Based ◽  
Information And Communication ◽  
E Learning ◽  
Knowledge Intensive

Traditional teaching and learning methods have had to adapt to keep up with Information and Communication Technologies (ICTs) in modern society. E-learning stands for all forms of Web-based learning and uses computer and computer networks to create, store, deliver, manage and support online learning courses to anyone, anytime and anywhere. It provides a configurable infrastructure that can integrate learning materials, tools, and services into a single solution to create and deliver training or educational materials quickly, effectively, and economically. Recently, emerging Semantic Web technologies have changed the focus of e-learning systems from task-based approaches to knowledge-intensive ones. The Semantic Web is a W3C initiative and according to Berners-Lee et al. (2001) comprises “an extension of the current Web in which information is given welldefined meaning, better enabling computers and people to work in cooperation”. The capability of the Semantic Web to add meaning to information, stored in such way that it can be searched and processed, as well as recent advances in Semantic Web-based technologies provide the mechanisms for semantic knowledge representation, exchange and collaboration of e-learning applications (Anderson & Whitelock, 2004).

Supporting Evolution of Knowledge Artifacts in Web Based Learning Environments

2009 ◽  
pp. 142-167
Author(s):  
Dimitris Kotzinos ◽  
Giorgos Flouris ◽  
Yannis Tzitzikas
Keyword(s):  
Semantic Web ◽  
Learning Environments ◽  
Knowledge Creation ◽  
Common Knowledge ◽  
Learning Theories ◽  
Web Based ◽  
Knowledge Repository ◽  
Web Based Learning ◽  
E Learning ◽  
Collaborative Knowledge

The development of collaborative e-learning environments that support the evolution of semantically described knowledge artifacts is a challenging task. In this chapter we elaborate on usage scenarios and requirements for environments grounded on learning theories that stress on collaborative knowledge creation activities. Subsequently, we present a comprehensive suite of services, comprising an emerging framework, called Semantic Web Knowledge Middleware (SWKM), that enables the collaborative evolution of both domain abstractions and conceptualizations, and data classified using them. The suite includes advanced services for ontology change, comparison and versioning over a common knowledge repository offering persistent storage and validation.

scholarly journals A Web-Based E-Learning System Using Semantic Web Framework

2006 ◽  
Vol 2 (8) ◽  
pp. 619-626 ◽  
Author(s):  
Fayed F. M. Ghaleb ◽  
Sameh S. Daoud ◽  
Ahmad M. Hasna ◽  
Jihad M. Jaam ◽  
Hosam F. El-Sofany
Keyword(s):  
Semantic Web ◽  
Learning System ◽  
Web Based ◽  
Web Framework ◽  
E Learning

scholarly journals Design and Implementation of Adaptive Recommendation System

2018 ◽  
pp. 101-117
Author(s):  
MagedEla zony ◽  
Ahmed Khalifa ◽  
Sayed Nouh ◽  
Mohamed Hussein
Keyword(s):  
Semantic Web ◽  
Semantic Similarity ◽  
Recommendation System ◽  
Learning Objects ◽  
Just In Time ◽  
Web Based ◽  
Design And Implementation ◽  
Knowledge Based ◽  
E Learning ◽  
Learner Profile

E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study.In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.

Using Ontology for Personalized E-Learning in K-12 Education

2010 ◽  
pp. 301-309 ◽  
Author(s):  
Petek Askar ◽  
Arif Altun ◽  
Kagan Kalinyazgan ◽  
S. Serkan Pekince
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Environments ◽  
Ontology Development ◽  
Future Directions ◽  
Web Based ◽  
Implementation Processes ◽  
Web Environment ◽  
E Learning ◽  
K 12

This chapter introduces the development of a K-12 education ontology for e-learning environments. It presents design and implementation processes, followed by several recommendations for future directions for ontology development. E-learning environments incorporate the notion of semantic Web-based ontologies into their future directions. Semantic Web uses ontologies to show the interconnectedness in a Web environment. Within the concept of semantic mapping, domain ontology is at the core of intelligent e-learning systems. In order to achieve an ontology for K-12 education, the authors propse a domainspecific ontology PoleONTO (Personalized Ontological Learning Environment) with the emphasis on its development and incorporation into an e-learning environment.

Classroom for the Semantic Web

2005 ◽  
pp. 251-283 ◽  
Author(s):  
Goran Shimic ◽  
Dragan Gasevic ◽  
Vladan Devedzic
Keyword(s):  
Semantic Web ◽  
Petri Net ◽  
Learning Systems ◽  
Learning Materials ◽  
Semantic Web Technologies ◽  
Web Based ◽  
Tutoring Systems ◽  
Web Technologies ◽  
E Learning ◽  
Server System

This chapter emphasizes integration of Semantic Web technologies in intelligent learning systems by giving a proposal for an intelligent learning management system (ILMS) architecture we named Multitutor. This system is a Web-based environment forth development of e-learning courses and for the use of them by the students. Multitutor is designed as a Web-classroom client-server system, ontologically founded, and is built using modern intelligent and Web-related technologies. This system enables the teachers to develop tutoring systems for any course. The teacher has to define the metadata of the course: chapters, the lessons and the tests, the references of the learning materials. We also show how the Multitutor system can be employed to develop learning systems that use ontologically created learning materials as well as Web services. As an illustration we describe a simple Petri net teaching system that is based on the Petrinet infrastructure for the Semantic Web.

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