为了优化标准化工作流程,提高标准化工作效率,推动标准数字化发展,介绍了大语言模型(Large Language Model,LLM)在智能问答中的演进与创新,利用大语言模型和检索增强生成(Retrieval-Augmented Generation,RAG)技术,构建了一个标准文献...为了优化标准化工作流程,提高标准化工作效率,推动标准数字化发展,介绍了大语言模型(Large Language Model,LLM)在智能问答中的演进与创新,利用大语言模型和检索增强生成(Retrieval-Augmented Generation,RAG)技术,构建了一个标准文献智能问答解决方案,可通过对标准文档的深入理解和智能化处理,实现对复杂标准问题的准确回答,从而增强标准文献的应用价值和实际效益。展开更多
To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance...To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.展开更多
基金National Key R&D Program of China (2018AAA0102100)Hunan Provincial Department of Education Outstanding Youth Project (22B0385)2022 Disciplinary Construction “Revealing the List and Appointing Leaders” Project(22JBZ051)。
文摘为了优化标准化工作流程,提高标准化工作效率,推动标准数字化发展,介绍了大语言模型(Large Language Model,LLM)在智能问答中的演进与创新,利用大语言模型和检索增强生成(Retrieval-Augmented Generation,RAG)技术,构建了一个标准文献智能问答解决方案,可通过对标准文档的深入理解和智能化处理,实现对复杂标准问题的准确回答,从而增强标准文献的应用价值和实际效益。
文摘To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.
