# SeFNet: Bridging Tabular Datasets with Semantic Feature Nets

arXiv: https://arxiv.org/abs/2306.11636 · HTML: https://mlsanity.com/paper/2306.11636/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Towards a Neural Statistician](https://mlsanity.com/paper/1606.02185/) (2016)
- [AMLB: an AutoML Benchmark](https://mlsanity.com/paper/2207.12560/) (2022)
- [Dataset2Vec: Learning Dataset Meta-Features](https://mlsanity.com/paper/1905.11063/) (2019)
- [AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning](https://mlsanity.com/paper/1802.07207/) (2018)
- [Semantic Similarity from Natural Language and Ontology Analysis](https://mlsanity.com/paper/1704.05295/) (2017)
- [Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV](https://mlsanity.com/paper/2201.11815/) (2022)
- [Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts](https://mlsanity.com/paper/2004.06555/) (2020)

## Built on by (most cited first, 1 of 1)

- [Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data](https://mlsanity.com/paper/2308.11446/) (2023)

## Abstract (arXiv)

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.