Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913481717972992 |
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| author | Lin, Kate Alrashed, Tarfah Noy, Natasha |
| author_facet | Lin, Kate Alrashed, Tarfah Noy, Natasha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14636 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web Lin, Kate Alrashed, Tarfah Noy, Natasha Information Retrieval Computation and Language Human-Computer Interaction Machine Learning 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. |
| title | Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web |
| topic | Information Retrieval Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2408.14636 |