Exploration of Question-Answering Systems: Survey

Abstract

This paper presents a comprehensive examination of the varied strategies employed in converting natural language into SPARQL queries, a critical component within Question Answering Systems (QAS). The exploration encompasses a broad spectrum of methods, encompassing rule-based, template-based, machine learning-driven, ontology-based, and hybrid approaches. By meticulously delving into the strengths and limitations of each method, the paper offers valuable insights into their practical implications. Furthermore, the provision of illustrative examples, such as PowerAqua, enriches the comprehension of these methods in real-world contexts. The paper serves to inform the selection of methods based on the specific requirements of the application and contextual constraints, thus contributing to informed decision-making in implementing QAS.