In recent years, advanced machine learning and artificial intelligence techniques have gained popularity due to their ability to solve problems across various domains with high performance and quality. However, these techniques are often so complex that they fail to provide simple and understandable explanations for the outputs they generate. To address this issue, the field of explainable artificial intelligence has recently emerged. On the other hand, most data generated in different domains are inherently structural; that is, they consist of parts and relationships among them. Such data can be represented using either a simple data-structure or form, such as a vector, or a complex data-structure, such as a graph. The effect of this representation form on the explainability and interpretability of machine learning models is not extensively discussed in the literature. In this survey paper, we review efficient algorithms proposed for learning from inherently structured data, emphasizing how their representation form affects the explainability of learning models. A conclusion of our literature review is that using complex forms or data-structures for data representation improves not only the learning performance, but also the explainability and transparency of the model.

近年来，先进的机器学习和人工智能技术因其能够以高性能和高质量解决各个领域的问题而受到欢迎。然而，这些技术通常非常复杂，以至于无法为其生成的输出提供简单且易于理解的解释。为了解决这个问题，可解释的人工智能领域最近出现了。另一方面，不同领域生成的大多数数据本质上都是结构化的；也就是说，它们由各个部分以及它们之间的关系组成。此类数据可以使用简单的数据结构或形式（例如向量）或复杂的数据结构（例如图）来表示。这种表示形式对机器学习模型的可解释性和可解释性的影响在文献中并未得到广泛讨论。在这篇调查论文中，我们回顾了为从固有结构化数据中学习而提出的有效算法，强调了它们的表示形式如何影响学习模型的可解释性。我们文献综述的结论是，使用复杂的形式或数据结构进行数据表示不仅可以提高学习性能，还可以提高模型的可解释性和透明度。