Semantic Self Learning And Teaching Agent (SESLATA)

Abstract

Semantic Self Learning And Teaching Agent (SESLATA) is a self learning software which is capable of learning from a natural language source. It identifies language complexity, ambiguity and influence of diverse writing styles to extract and decipher. The specialty herein the system is, usage of its acquired knowledge to perform teaching and explaining activities to its end users. The agent is capable of updating its own knowledge and it interacts with learner through intelligent response and using own experiences in the process of teaching according to learner's knowledge. Simply it learns somewhat like a human and teaches what it has learnt as a human does. The software is endowed with inventions involving in Natural Language Processing, machine learning, explanation and knowledge representation and ontology, which are still under research. Self learning from natural language (acquiring the domain knowledge to model ontology), automate the ontology creation from natural language, the teaching and explaining capability of an agent, updating the knowledge via ontology, knowledge representation and sharing, a new methodology of online learning, teaching according to the depth of user's knowledge are the major findings of the system.