Fuzzy Semantic Classification of Multi-Domain E-Learning Concept

Abstract

Scholarly articles are a great source of knowledge. Learning from them like E-learning requires automatic approaches to build concept-maps, learning paths, etc., as these sources are monotonically increasing and are big too. These sources have multi-domain, variety, huge volumes, which are, in fact, Big Data’s characteristics. Thus data from different domains have to be handled together, especially in the E-learning systems. This paper presents a new approach for concept extraction and semantically clustering and classification of these e-learning concepts using fuzzy membership values. Scholarly articles from different domains are taken for our experimental work, and we tested on BBC datasets with 100 documents and 650 documents. Since the number of domains is known, and all concepts are stored, we have done both clustering and classification for testing our fuzzy-based semantic system. We have used logistics regression, Support Vector Machine (SVM) with Linear kernel, Polynomial kernel, Radial Basis Function (RBF) Kernel, Sigmoid kernel to obtain maximum accuracy up to 94% to 96% for all data sets. In clustering, using K-Means, we got precision up to 93%. The system can be used to generate adaptive learning paths, concept map extraction; Big-Data based E-Learning portals.