Enhancing Question Answering through Augmented Term Extraction on Generated Ontology in Closed Domain - Bali, Vikas; Verma, Amandeep (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The ever-increasing use of smartphones and computers has led to a culture of instant gratification, shaping the way information is sought and shared in today's digital age. Natural language understanding (NLU) is the linchpin that allows machines to access, process, and provide answers from the vast amount of human knowledge stored in natural language (NL) text. The ongoing development of NLU technologies continues to drive advances in question answering and a wide range of other natural language processing (NLP) applications. Ontology plays a pivotal role in question answering by enhancing the system's ability to understand, contextualize, and retrieve relevant information from a structured knowledge base or unstructured textual data. In this work, we designed an Ontology-based Question Answering (QA) using a self-created dataset is an interesting and valuable endeavour. The system can leverage augmented term extraction on automatically created ontology to understand the domain context and relationships between concepts and is well integrated into the QA system. The performance of QA prototype is accessed using appropriate evaluation metrics This involve using a portion of self-created dataset as a test set and comparing the system's answers to the ground truth.