The main aim of the proposed work is to design an automated approach to extract domain ontology from the e-learning resources. The domain ontology derived is embedded into the e-learning system so that the learners can be provided with more relevant and meaningful learning materials based on their knowledge levels. The advantage of the proposed approach is the concept map extraction that is effectively achieved through the use of association rule mining algorithm and fuzzy techniques. Experimentations were carried out to prove the efficiency in constructing the domain ontology and the performance is analysed with the previous approach. Simulation experiments were carried out to implement the automated domain ontology to the adaptive e-learning system. © 2014 Inderscience Enterprises Ltd.

Saleena B

Computer Science

School of Computer Science and Engineering

Chennai Campus

Srivatsa S.K.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

An automated approach to extract domain ontology for an e-learning system

International Journal of Innovation and Learning

e-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert's ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods. © 2014 King Saud University.

Fulltext

Journal of King Saud University - Computer and Information Sciences

Using concept similarity in cross ontology for adaptive e-Learning systems

E-Learning deals with the interaction between the teacher and learner on the basis of knowledge possessed by the learner. Ontology is a key concept that helps in providing more relevant and accurate information to the users for a particular domain or from multiple domains. Finding semantic similarity between the concepts of different ontologies will enhance the integration and retrieval of resources in a more meaningful way. In this paper, an adaptive e-learning system is proposed, which generates use r-specific e-learning contents based on their knowledge level and by comparing the similarity of concepts between ontologies. Based on this a new approach is designed to retrieve more relevant and accurate documents by embedding a new Concept Similarity measure (ConSim), after evaluating the user's knowledge based on the search query. The experimental results prove that after embedding the ConSim measure the retrieval results have been improved. Copyright © 2014 Inderscience Enterprises Ltd.

International Journal of Metadata, Semantics and Ontologies

Designing a novel cross ontology measure to extract resources from knowledge base for an adaptive e-learning system

Ontology has become the key concept for providing relevant and accurate information to the users for a particular domain or from multiple domains. To extract similar concepts from multiple ontologies, there is a need to develop a concept similarity measure between two independent ontologies. The intention of finding semantic similarity is to enhance the integration and retrieval of resources in a more meaningful and accurate way. Based on the existing hybrid similarity measures an enhanced similarity measure is proposed which brings out a more accurate relationship between the two concepts of different ontologies, the proposed measure is equivalent to human judgments. The proposed method has an improved technique over other methods. The experimental results establish the efficiency of our proposed work and reveals that similarity measure for cross ontology out performs the current hybrid similarity measures by 20% in terms of correlation coefficients. Copyright 2012 ACM.

Proceedings of the CUBE International Information Technology Conference on - CUBE '12

ConSim

International Journal of Computer Applications

A Novel Approach to Develop a Self-Organized Domain Specific Search Mechanism for Knowledge Acquisition using Ontology

Proceedings of the 11th International Conference of the NZ Chapter of the ACM Special Interest Group on Human-Computer Interaction on ZZZ - CHINZ '10

Supporting adaptive learning interactions with ontologies

Enhancing a Mechatronics design distance-learning course through internet and Web 2.0 technologies

Distant control of student skills by formal model of domain knowledge

Mobile learning: the economics perspective

Journal	International Journal of Innovation and Learning
Publisher	Inderscience Publishers
ISSN	1471-8197
Open Access	No