2024/08/26 by Kate Lin, Tarfah Alrashed, Lin, Kate +3 · 1 citation
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #Data Quality and Management #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2408.14636
openalex publication_date 2024/08/26 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28
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.