Knowledge Graph semantic enhancement of input data for improving AI
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 input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability.
Cite
@article{arxiv.2005.04726,
title = {Knowledge Graph semantic enhancement of input data for improving AI},
author = {Shreyansh Bhatt and Amit Sheth and Valerie Shalin and Jinjin Zhao},
journal= {arXiv preprint arXiv:2005.04726},
year = {2020}
}