Link Prediction with Supervised Learning on an Industry 4.0 related Knowledge Graph

Irlán Grangel-González, Fasal Shah
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引用次数: 1

Abstract

Industry 4.0 requires the integration of many actors to provide correct, personalized, and quick answers to customers. In order to meet this integration, data coming from different actors demand to be semantically integrated and harmonized. In these settings, knowledge graphs have proven to be successful in the task of semantic data integration of distinct data silos. Despite the increasing adoption of knowledge graphs in the Industry 4.0 domain for integrating and harmonizing data, still, all the power of the integrated data is not exploited. In this article, we tackle the problem of knowledge graph completion presenting an approach that applies supervised machine learning algorithms on top of the knowledge graph. In general, observed results indicate that supervised machine learning algorithms perform with an AUC of more than 88%. These outcomes suggest that knowledge graph completion enables to unveil new relations by connecting entities in the knowledge graph. Thus, the discovered relations in the knowledge graph bring added value to the Industry 4.0 domain.
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在工业4.0相关知识图上将预测与监督学习联系起来
工业4.0需要整合许多参与者,为客户提供正确、个性化和快速的答案。为了满足这种集成,来自不同参与者的数据需要在语义上进行集成和协调。在这些设置中,知识图已被证明在不同数据孤岛的语义数据集成任务中是成功的。尽管在工业4.0领域越来越多地采用知识图谱来集成和协调数据,但集成数据的所有功能仍未得到充分利用。在本文中,我们解决了知识图完成的问题,提出了一种在知识图上应用监督机器学习算法的方法。总的来说,观察结果表明,监督机器学习算法的AUC超过88%。这些结果表明,知识图谱补全能够通过连接知识图谱中的实体来揭示新的关系。因此,知识图谱中发现的关系为工业4.0领域带来了附加价值。
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