Constructing a KBQA Framework: Design and Implementation

The exponential growth of data on the internet has made information retrieval increasingly challenging. Knowledge-based Question-Answering (KBQA) framework offers an efficient solution that quickly provides accurate and relevant information. However, these frameworks face significant challenges, especially when dealing with complex queries involving multiple entities and properties. This paper studies KBQA frameworks, focusing on improving entity recognition, property extraction, and query generation using advanced Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques. We implemented and evaluated combination tools for extracting entities and properties, with the combination of models achieving the best performance. Our evaluation metrics included entity and property retrieval, SPARQL query completeness, and accuracy. The results demonstrated the effectiveness of our approach, with high accuracy rates in identifying entities and properties.