QUASE: AN Ontology-Based Domain Specific Natural Language Question Answering System
Vaibhav Mishra1, Nitesh Khilwani2
1Vaibhav Mishra, Research Scholar, Department of Computer Science & Engineering, Mewar University, Chittorgarh, Rajasthan, India.
2Dr. Nitesh Khilwani , Technical Architect, Edifecs Round Glass, Noida, India. 

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 261-268 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6773118419/2019©BEIESP | DOI: 10.35940/ijrte.D6773.118419

Open Access | Ethics and Policies | Cite  | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Since early days Question Answering (QA) has been an intuitive way of understanding the concept by humans. Considering its inevitable importance it has been introduced to children from very early age and they are promoted to ask more and more questions. With the progress in Machine Learning & Ontological semantics, Natural Language Question Answering (NLQA) has gained more popularity in recent years. In this paper QUASE (QUestion Answering System for Education) question answering system for answering natural language questions has been proposed which help to find answer for any given question in a closed domain containing finite set of documents. The QA system mainly focuses on factoid questions. QUASE has used Question Taxonomy for Question Classification. Several Natural Language Processing techniques like Part of Speech (POS) tagging, Lemmatization, Sentence Tokenization have been applied for document processing to make search better and faster. DBPedia ontology has been used to validate the candidate answers. By application of this system the learners can gain knowledge on their own by getting precise answers to their questions asked in natural language instead of getting back merely a list of documents. The precision, recall and F measure metrics have been taken into account to evaluate the performance of answer type evaluation. The metric Mean Reciprocal Rank has been considered to evaluate the performance of QA system. Our experiment has shown significant improvement in classifying the questions in to correct answer types over other methods with approximately 91% accuracy and also providing better performance as a QA system in closed domain search.
Keywords: DBPedia Ontology, Question Answering System, Question Classification, QUASE, Natural Language Processing, Machine Learning.
Scope of the Article: Machine Learning.