Knowledge Graph Semantic Enhancement of Input Data for Improving AI
Document Type
Article
Publication Date
3-1-2020
Abstract
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.
Repository Citation
Bhatt, S.,
Sheth, A.,
Shalin, V.,
& Zhao, J.
(2020). Knowledge Graph Semantic Enhancement of Input Data for Improving AI. IEEE Internet Computing, 2 (24), 66-72.
https://corescholar.libraries.wright.edu/psychology/543
DOI
10.1109/MIC.2020.2979620
