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.

本文介绍了一种称为 Semantic Feature Net (SeFNet) 的新方法，其通过使用现有的本体和领域知识来捕捉分析表格特征的语义含义，提高了元学习的潜力并允许在不同的预测任务之间共享有价值的见解。