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Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user"s query demonstrating the effectiveness of the proposed approach.

The current learning systems typically lack the level of meta-cognitive awareness, self-directed learning, and time management skills. Most of the ontologically based learning management systems are in the proposed phase and those which are developed do not provide the necessary path guidance for proper learning. The systems available are not as adaptive from the viewpoint of the learner as required. Ontology engineering has become an important pillar for knowledge management and representation in recent years. The design, approach and implementation of ontology in elearning and m-learning systems have made them more effective. In this paper, we have proposed a system for the betterment of knowledge management and representation of associated data as compared to the previously available learning management systems. Here, we have presented the application and implementation of ontological engineering methodology in the Computer Science domain. For knowledge management, we have created a domain associated ontology which represents knowledge of a single domain. Subsequently, ontology has been created to manage a learner profile so that a learner may be aligned to a proper path of learning. The learner ontology will use the VARK learning model which classifies what kind of learning does the learner requires so that necessary resources could be provided.

Abstract. One of the challenges in information retrieval is providing accurate answers to a user's question often expressed as uncertainty words. Most answers are based on a Syntactic approach rather than a Semantic analysis of the query. In this paper our objective is to present a hybrid approach for a Semantic question answering retrieval system using Ontology Similarity and Fuzzy logic. We use a Fuzzy co-clustering algorithm to retrieve collection of documents based on Ontology Similarity. Fuzzy scale uses Fuzzy type-1 for documents and Fuzzy type-2 for words to prioritize answers. The objective of this work is to provide retrieval systems with more accurate answers than nonfuzzy Semantic Ontology approach.

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