A Proposed Multi-Domain Approach for Automatic Classification of Text Documents

Abdelrahman M. Arab, Ahmed M. Gadallah and Akram Salah, Cairo University, Egypt

ABSTRACT

Classification is an important technique used in information retrieval. Supervised classification suffers from certain limitations concerning the collection and labeling of the training dataset. When facing MultiDomain classification, multiple training datasets and classifiers are needed which is relatively difficult. In this paper an unsupervised classification system is proposed that can manage the Multi-Domain classification problem as well. It is a multi-domain system where each domain represented by an ontology. A document is mapped on each ontology based on the weights of the mutual tokens between them with the help of fuzzy sets, resulting in a mapping degree of the document with each domain. An experiment carried out showing satisfying classification results with an improvement in the evaluation results of the proposed system compared to Apache Lucene.

KEYWORDS

Information Retrieval, Ontology, Machine Learning, Document Classification, Fuzzy Sets 

Original Source URL: https://aircconline.com/ijsc/V8N1/8117ijsc01.pdf

https://airccse.org/journal/ijsc/current2017.html

Adaptive Learning Management System Using Semantic Web Technologies

M.Farida Begam1 and Gopinath Ganapathy2, 1Manipal University Dubai, U.A.E and 2Bharathidasan University, India

ABSTRACT

Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended.

KEYWORDS

Learning Style, Learning Objects, workflow, web services, Semantics, Ontology, LMS, ALMS, and OWL-S

Original Source URL: https://airccse.org/journal/ijsc/papers/4113ijsc01.pdf

https://airccse.org/journal/ijsc/current2013.html

ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES

ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES 

M.Farida Begam1 and Gopinath Ganapathy2 1Department of Information Technology, Manipal University Dubai, U.A.E 2 School of Computer Science and Engineering, Bharathidasan University, India

ABSTRACT 

Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended. 

KEYWORDS 

Learning Style, Learning Objects, workflow, web services, Semantics, Ontology, LMS, ALMS, and OWL-S

Original Source URL : http://airccse.org/journal/ijsc/papers/4113ijsc01.pdf

http://airccse.org/journal/ijsc/current2013.html