Machine learning applications cover a wide range of predictive tasks in which tabular datasets play a significant role. However, although they often address similar problems, tabular datasets are typically treated as standalone tasks. The possibilities of using previously solved problems are limited due to the lack of structured contextual information about their features and the lack of understanding of the relations between them. To overcome this limitation, we propose a new approach called Semantic Feature Net (SeFNet), capturing the semantic meaning of the analyzed tabular features. By leveraging existing ontologies and domain knowledge, SeFNet opens up new opportunities for sharing insights between diverse predictive tasks. One such opportunity is the Dataset Ontology-based Semantic Similarity (DOSS) measure, which quantifies the similarity between datasets using relations across their features. In this paper, we present an example of SeFNet prepared for a collection of predictive tasks in healthcare, with the features' relations derived from the SNOMED-CT ontology. The proposed SeFNet framework and the accompanying DOSS measure address the issue of limited contextual information in tabular datasets. By incorporating domain knowledge and establishing semantic relations between features, we enhance the potential for meta-learning and enable valuable insights to be shared across different predictive tasks.

翻译：机器学习应用涵盖了广泛的预测任务，其中表格数据集扮演着重要角色。然而，尽管这些数据集通常解决相似的问题，但它们往往被视为独立任务。由于缺乏关于其特征的结构化上下文信息以及对其间关系的理解，利用先前已解决问题的方法受到限制。为克服这一局限，我们提出了一种名为语义特征网络（SeFNet）的新方法，该方法能够捕捉所分析表格特征的语义含义。通过利用现有本体和领域知识，SeFNet为在不同预测任务间共享洞察开辟了新机遇。其中一个典型应用是数据集本体语义相似度（DOSS）度量，该度量通过特征间的关系量化数据集之间的相似性。本文中，我们展示了专为医疗领域一系列预测任务构建的SeFNet实例，其特征关系源于SNOMED-CT本体。所提出的SeFNet框架及配套的DOSS度量有效解决了表格数据集中上下文信息有限的问题。通过整合领域知识并建立特征间的语义关系，我们增强了元学习的潜力，并使得不同预测任务间能够共享有价值的洞察。