# Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web

arXiv: https://arxiv.org/abs/2408.14636 · HTML: https://mlsanity.com/paper/2408.14636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Scaling Up Models and Data with textttt5x and textttseqio](https://mlsanity.com/paper/2203.17189/) (2022)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [Google Dataset Search by the Numbers](https://mlsanity.com/paper/2006.06894/) (2020)
- [Sparse Projection Oblique Randomer Forests](https://mlsanity.com/paper/1506.03410/) (2015)
- [DeepJoin: Joinable Table Discovery with Pre-trained Language Models](https://mlsanity.com/paper/2212.07588/) (2022)
- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://mlsanity.com/paper/2212.02934/) (2022)
- [Lost or found? Discovering data needed for research](https://mlsanity.com/paper/1909.00464/) (2019)
- [NewsEmbed: Modeling News through Pre-trained Document Representations](https://mlsanity.com/paper/2106.00590/) (2021)
- [Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V (Technical Report)](https://mlsanity.com/paper/2301.13095/) (2023)

## Abstract (arXiv)

The Web today has millions of datasets, and the number of datasets continues to grow at a rapid pace. These datasets are not standalone entities; rather, they are intricately connected through complex relationships. Semantic relationships between datasets provide critical insights for research and decision-making processes. In this paper, we study dataset relationships from the perspective of users who discover, use, and share datasets on the Web: what relationships are important for different tasks? What contextual information might users want to know? We first present a comprehensive taxonomy of relationships between datasets on the Web and map these relationships to user tasks performed during dataset discovery. We develop a series of methods to identify these relationships and compare their performance on a large corpus of datasets generated from Web pages with schema.org markup. We demonstrate that machine-learning based methods that use dataset metadata achieve multi-class classification accuracy of 90%. Finally, we highlight gaps in available semantic markup for datasets and discuss how incorporating comprehensive semantics can facilitate the identification of dataset relationships. By providing a comprehensive overview of dataset relationships at scale, this paper sets a benchmark for future research.