论文标题
知识图语义增强输入数据以改善AI
Knowledge Graph semantic enhancement of input data for improving AI
论文作者
论文摘要
使用机器学习算法设计的智能系统需要大量的标记数据。背景知识提供了互补的，现实世界中的事实信息，可以增加有限的标记数据来训练机器学习算法。对于许多实际应用，知识图（kg）一词很流行，以图形的形式组织此背景知识非常方便且有用。最近的学术研究和实施的工业智能系统显示了将培训数据与知识图相结合的机器学习算法的有希望的性能。在本文中，我们讨论了相关KGS来增强使用机器学习的两个应用程序的输入数据 - 建议和社区检测。 KG提高了准确性和解释性。
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 input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability.
