Knowledge Graph semantic enhancement of input data for improving AI
This work addresses data scarcity for AI developers by integrating background knowledge, but it is incremental as it applies existing KG methods to specific domains.
The paper tackles the problem of limited labeled data for machine learning by using Knowledge Graphs (KGs) to enhance input data, showing improved accuracy and explainability in recommendation and community detection applications.
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.