Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real-world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance the input data for two applications that use machine learning—recommendation and community detection. The KG improves both accuracy and explainability.

中文翻译：

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用于改进人工智能的输入数据的知识图语义增强

使用机器学习算法设计的智能系统需要大量标记数据。背景知识提供了补充的、真实世界的事实信息，可以扩充有限的标记数据以训练机器学习算法。知识图谱 (KG) 一词在许多实际应用中都很流行，以图的形式组织这些背景知识既方便又有用。最近的学术研究和实施的工业智能系统已经显示出将训练数据与知识图相结合的机器学习算法的良好性能。在本文中，我们讨论了使用相关 KG 来增强两个使用机器学习的应用程序的输入数据——推荐和社区检测。KG 提高了准确性和可解释性。