Papers › MAGIC: Mining an Augmented Graph using INK, starting from a CSV
MAGIC: Mining an Augmented Graph using INK, starting from a CSV
Bram Steenwinckel, Filip De Turck, Femke Ongenae
A large portion of structured data does not yet reap the benefits of the Semantic Web. Therefore, The “Tabular Data to Knowledge Graph Matching” competition at ISWC tries to bridge this gap by evaluating and promoting the creation of such semantic annotations tools. Besides annotating data semantically, the system should also be able to further augment the datasets based on the provided annotations. In this paper, we propose a system that is capable of both annotating and augmenting a dataset by using the interpretable embedding technique INK. The “Tabular Data to Knowledge Graph Matching” competition was used to evaluate the proposed annotation capabilities of our proposed system.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cell Entity Annotation | BiodivTab | MAGIC | F1 (%) | 10 | #5 of 5 | Archive leaderboard | report |
| Cell Entity Annotation | ToughTables-DBP | MAGIC | F1 (%) | 18.4 | #5 of 5 | Archive leaderboard | report |
| Column Type Annotation | BiodivTab | MAGIC | F1 (%) | 14.2 | #5 of 6 | Archive leaderboard | report |
| Column Type Annotation | ToughTables-DBP | MAGIC | F1 (%) | 15.9 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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