近年来,基于知识图谱(KG)的服务推荐方法取得了显著进展,但现有方法的性能仍受限于节点信息属性的质量和图结构的局限性,尤其是在实体描述信息不足和大量无关三元组噪声的影响下,推荐准确性受到影响。为此,我们提出了一种结合大语言模型...近年来,基于知识图谱(KG)的服务推荐方法取得了显著进展,但现有方法的性能仍受限于节点信息属性的质量和图结构的局限性,尤其是在实体描述信息不足和大量无关三元组噪声的影响下,推荐准确性受到影响。为此,我们提出了一种结合大语言模型(LLM)的知识结构更新服务推荐方法。基于思维链设计提示策略,进一步增强了大语言模型在扩展知识图数据描述信息中的输出效果,从而全面提升了实体数据的语义表达能力。此外,设计了一种知识感知重构任务,该任务有效识别知识图谱中的关键关联子图,并削弱无用三元组对推荐结果的负面影响,进而优化图结构以提升推荐性能。在真实数据集上进行实验结果表明,我们的方法在面向mashup的API服务推荐优于几种先进的基线方法。In recent years, service recommendation methods based on knowledge graph (KG) have made remarkable progress, but the performance of existing methods is still limited by the quality of node information attributes and the limitations of graph structure, especially under the influence of insufficient entity description information and a lot of irrelevant triplet noise, which affects the recommendation accuracy. Therefore, we propose a knowledge structure update service recommendation method combined with a large language model (LLM). The suggestion strategy based on thought chain design further enhances the output effect of large language model in expanding knowledge graph data description information, thus comprehensively improving the semantic expression ability of entity data. In addition, a knowledge perception reconstruction task is designed, which effectively identifies the key association subgraphs in the knowledge graph, weakens the negative impact of useless triples on the recommendation results, and then optimizes the graph structure to improve the recommendation performance. Experimental results on real data sets show that our approach is superior to several advanced baseline approaches for Mashup-oriented API service recommendations.展开更多
文摘近年来,基于知识图谱(KG)的服务推荐方法取得了显著进展,但现有方法的性能仍受限于节点信息属性的质量和图结构的局限性,尤其是在实体描述信息不足和大量无关三元组噪声的影响下,推荐准确性受到影响。为此,我们提出了一种结合大语言模型(LLM)的知识结构更新服务推荐方法。基于思维链设计提示策略,进一步增强了大语言模型在扩展知识图数据描述信息中的输出效果,从而全面提升了实体数据的语义表达能力。此外,设计了一种知识感知重构任务,该任务有效识别知识图谱中的关键关联子图,并削弱无用三元组对推荐结果的负面影响,进而优化图结构以提升推荐性能。在真实数据集上进行实验结果表明,我们的方法在面向mashup的API服务推荐优于几种先进的基线方法。In recent years, service recommendation methods based on knowledge graph (KG) have made remarkable progress, but the performance of existing methods is still limited by the quality of node information attributes and the limitations of graph structure, especially under the influence of insufficient entity description information and a lot of irrelevant triplet noise, which affects the recommendation accuracy. Therefore, we propose a knowledge structure update service recommendation method combined with a large language model (LLM). The suggestion strategy based on thought chain design further enhances the output effect of large language model in expanding knowledge graph data description information, thus comprehensively improving the semantic expression ability of entity data. In addition, a knowledge perception reconstruction task is designed, which effectively identifies the key association subgraphs in the knowledge graph, weakens the negative impact of useless triples on the recommendation results, and then optimizes the graph structure to improve the recommendation performance. Experimental results on real data sets show that our approach is superior to several advanced baseline approaches for Mashup-oriented API service recommendations.
