基于ChatGLM和提示微调的旅游知识图谱构建
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新疆财经大学信息管理学院

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TP391.1

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Tourism Knowledge Graph Construction Based on ChatGLM and Prompt-tuning
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School of Information Management,Xinjiang University of Finance and Economics

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    摘要:

    为缓解旅游领域知识分散、信息碎片化的问题,提出一种基于ChatGLM(chat generative language model)和提示微调的实体关系抽取模型ChatGLM-ppt(fine-tuning ChatGLM with prompt and p-tuning)。该模型借助ChatGLM以对话形式完成实体关系抽取任务,并通过P-Tuning v2微调和添加提示模板的方法应对实体关系抽取中错误传播、实体冗余和关系重叠等问题。实验建立在自建的旅游领域数据集上,结果表明:在旅游领域实体关系抽取问题上ChatGLM-ppt模型F1值为92.19%,在处理重叠关系问题中F1值均大于90%,优于目前主流的实体关系抽取模型,证明该模型可有效提高实体关系抽取的准确率。进一步运用Neo4j图数据库构建旅游知识图谱,整合分散的旅游信息资源,对促进旅游业的数字化转型和智能化发展具有一定的参考意义。

    Abstract:

    In order to alleviate the problem of knowledge dispersion and information fragmentation in the tourism domain, an entity relation extraction model ChatGLM-ppt (fine-tuning ChatGLM with prompt and p-tuning) based on ChatGLM (chat generative language model) and prompt fine-tuning is proposed. The model accomplishes the entity and relation extraction task in the form of dialog with the help of ChatGLM, and copes with the problems of error propagation, entity redundancy and relationship overlapping in entity and relation extraction by means of P-Tuning v2 fine-tuning and adding prompt templates. The experiments are built on the self-constructed tourism domain dataset, and the results show that the F1 value of the ChatGLM-ppt model is 92.19% in the entity and relation extractio n problem in the tourism domain, and the F1 values are all greater than 90% in dealing with overlapping relationships, which is better than that of the current mainstream entity and relation extraction models, proving that the model can effectively improve the accuracy of entity and relation extraction. Further use of Neo4j graph database to construct tourism knowledge graph and integrate dispersed tourism information resources has certain reference significance for promoting the digital transformation and intelligent development of tourism.

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徐春,苏明钰,孙彬. 基于ChatGLM和提示微调的旅游知识图谱构建[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-03-17
  • 最后修改日期:2024-05-28
  • 录用日期:2024-06-05
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