基于知识图和图卷积网络的制造服务推荐方法

IF 9.9 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qing Zheng , Tingfeng Guo , Guofu Ding , Haizhu Zhang , Kai Zhang
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引用次数: 0

摘要

服务型制造是制造与服务深度融合的新型制造模式,以制造服务平台的运行为支撑。这标志着先进制造业与现代服务业的紧密结合。在此背景下,新一代信息技术的进步促使服务提供者和需求者的参与度不断提高,导致制造业服务平台上可获得的信息大幅增加。在需求端,从大量制造服务中进行选择是一个挑战,而在服务提供端,从众多任务中进行选择是一个挑战。同时,制造服务推荐方法存在冷启动、信息利用不足、引入噪声、建模表示不充分等问题。针对上述问题,本研究提出了一种基于知识图和图卷积网络的制造服务推荐方法。该方法利用图卷积网络对知识图中制造服务提供者和任务的邻域信息进行聚合,并在聚合过程中引入自关注机制来降低噪声干扰。最后,将获得的邻域信息集成到制造服务提供者和任务的初始表示中,预测其交互的概率,从而实现制造服务推荐。使用特定平台的服务数据创建数据集进行验证,验证了所提出的服务推荐方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Manufacturing service recommendation method based on knowledge graph and graph convolutional network
Service-oriented manufacturing is a novel manufacturing model that involves a deep integration of manufacturing and services, facilitated by the operation of manufacturing service platforms. This signifies the tight integration between advanced manufacturing and modern service industries. In this context, the advancement of new-generation information technology has prompted increased participation from both service providers and demanders, leading to a significant expansion of information available on manufacturing services platforms. On the demand side, there is a challenge in selecting from a vast array of manufacturing services, while on the service provide side, there is a challenge in selecting from numerous tasks. At the same time, there are issues with manufacturing service recommendation methods, including cold start, insufficient information utilization, introduction of noise, and inadequate modeling representation. To address the aforementioned issues, this study proposes a manufacturing service recommendation method based on knowledge graphs and graph convolutional networks. The method utilizes graph convolutional networks to aggregate neighborhood information of manufacturing service providers and tasks in the knowledge graph, and introduces self-attention mechanism during the aggregation process to reduce noise interference. Finally, the obtained neighborhood information is integrated into the initial representation of manufacturing service providers and tasks, predicting the probability of their interaction, thus achieving manufacturing service recommendation. Using service data from a specific platform to create a dataset for validation, the effectiveness of the proposed service recommendation method has been demonstrated.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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