Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?
Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?
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DOI：
10.1109/mic.2020.3031769
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发表时间：
2021-01-01
影响因子：
3.2
通讯作者：
Sheth, Amit
中科院分区：
文献类型：
--
作者：
Gaur, Manas;Faldu, Keyur;Sheth, Amit
最近深度学习 (DL) 领域的一系列创新显示出对个人和社会产生积极和消极影响的巨大潜力。利用强大的计算能力和庞大的数据集的深度学习模型在计算机视觉、自然语言处理和人机交互等技术领域日益困难、定义明确的研究任务上显着优于以前的历史基准。然而，深度学习的黑盒性质以及对压缩为标签和密集表示的大量数据的过度依赖给可解释性和可解释性带来了挑战。此外，深度学习尚未证明其有效利用对人类理解至关重要的相关领域知识的能力。早期以数据为中心的方法缺少这一方面，因此需要知识注入学习（K-iL）来整合计算知识。本文演示了如何使用 K-iL 将知识（以知识图的形式提供）整合到 DL 中。通过医疗保健和教育领域的自然语言处理应用示例，我们讨论了 K-iL 在可解释性和可解释性方面的实用性。
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. 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, and human-computer interactions. However, DL's black-box nature and over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for interpretability and explainability. Furthermore, DLs have not proven their ability to effectively utilize relevant domain knowledge critical to human understanding. This aspect was missing in early data-focused approaches and necessitated knowledge-infused learning (K-iL) to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL using K-iL. Through examples from natural language processing applications in healthcare and education, we discuss the utility of K-iL towards interpretability and explainability.