Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web
<|reference_start|>Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web: 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.<|reference_end|>
@article{lin2024relationships,
    title={Relationships are Complicated! An Analysis of Relationships Between
  Datasets on the Web},
    author={Kate Lin, Tarfah Alrashed, Natasha Noy},
    journal={arXiv preprint arXiv:2408.14636},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14636},
    primaryClass={cs.IR cs.CL cs.HC cs.LG}
}