Research on Performance Improvement of Knowledge Graph Reasoning Algorithms in Question-Answering Systems

This paper studies the performance improvement of reasoning algorithms based on knowledge graphs in question-answering systems. This paper designs a reasoning algorithm that combines semantic understanding and natural language processing technology, and realizes the effective connection between questions and knowledge graphs through entity linking and relationship extraction, thereby improving the understanding and reasoning ability of the question-answering system. This paper pays special attention to the balance between reasoning efficiency and reasoning accuracy, and proposes an efficient knowledge reasoning model. This model uses graph neural networks to represent and learn nodes and edges in the knowledge graph, thereby optimizing the information flow in the reasoning process. This paper conducts simulation experiments on the model on a large-scale data set. The results show that compared with the traditional question-answering system, the reasoning algorithm based on the knowledge graph has achieved significant performance improvement in answering complex questions, with an 8.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> increase in question-answering accuracy and a 12.3% increase in reasoning speed. Through precise data analysis, this paper proves that the proposed reasoning algorithm can not only significantly improve the semantic understanding ability of the question-answering system, but also achieve effective optimization in performance.