Fuzzy Cross Domain Concept Mining

Abstract

E-Learning has emerged as an important research area. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. For creating Concept map, concepts are extracted. Concepts are domain dependent but big data can have data from different domains. Data in different domain has different semantics. So before applying any analytics to such big unstructured data, we have to categorize the important concepts domain wise semantically before applying any machine learning algorithm. In this paper, we have used a novel approach to automatically cluster the E-Learning concept semantically; we have shown the cluster in table format. Initially, we have extracted important concepts from unstructured data followed by generation of vector space of each concept. Then we used different similarity formula to calculate fuzzy membership values of elements of vector to its corresponding concepts. Semantic Similarity is calculated between two concepts by considering repeatedly the semantic similarity or information gain between two elements of each vector. Then Semantic similarity between two concepts is calculated. Thus concept map can be generated for a particular domain. We have taken research articles as our dataset from different domains like computer science and medical domain containing articles on Cancer. A graph is generated to show that fuzzy relationship between them for all domain. Then clustering them in based on their distances