Constructing a KBQA Framework: Design and Implementation

The exponential growth of data on the internet has made information retrieval increasingly challenging. Knowledge-based QuestionAnswering (KBQA) framework offers an efficient solution thatquickly provides accurate and relevant information. However, theseframeworks face significant challenges, especially when dealingwith complex queries involving multiple entities and properties.This paper studies KBQA frameworks, focusing on improving entity recognition, property extraction, and query generation usingadvanced Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques. We implemented and evaluated combinationtools for extracting entities and properties, with the combinationof models achieving the best performance. Our evaluation metricsincluded entity and property retrieval, SPARQL query completeness, and accuracy. The results demonstrated the effectiveness o four approach, with high accuracy rates in identifying entities andproperties