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.

学术文章是丰富的知识来源。向他们学习就像电子学习一样，需要自动的方法来构建概念图，学习路径等，因为这些资源单调地增加并且也很大。这些来源具有多领域，种类繁多，数量庞大的特点，这实际上是大数据的特征。因此，必须将来自不同域的数据一起处理，尤其是在电子学习系统中。本文提出了一种新的方法，用于使用模糊隶属度值对这些电子学习概念进行概念提取以及语义上的聚类和分类。来自不同领域的学术文章被用于我们的实验工作，并且我们在BBC数据集中测试了100个文档和650个文档。由于域的数量已知，并且所有概念都已存储，我们已经对测试基于模糊的语义系统进行了聚类和分类。我们使用了物流回归，带有线性核，多项式核，径向基函数（RBF）核，Sigmoid核的支持向量机（SVM），以对所有数据集获得高达94％到96％的最大精度。使用K-Means进行聚类时，我们的精度高达93％。该系统可用于生成自适应学习路径，概念图提取；基于大数据的在线学习门户。该系统可用于生成自适应学习路径，概念图提取；基于大数据的在线学习门户。该系统可用于生成自适应学习路径，概念图提取；基于大数据的在线学习门户。