Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

TL;DR

This paper explores how integrating knowledge graphs into deep learning models enhances their interpretability and explainability, addressing the black-box challenge by leveraging domain knowledge in NLP applications.

Contribution

It demonstrates the use of knowledge-infused learning with knowledge graphs to improve interpretability and explainability in deep learning systems.

Findings

Knowledge graphs can be effectively incorporated into DL models.

Knowledge-infused learning enhances model interpretability.

Applications in healthcare and education show practical benefits.

Abstract

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL methods using knowledge-infused learning, which is one of the strategies. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches, and illustrate it with examples from natural language processing for healthcare and education applications.