MAGIC: Mining an Augmented Graph using INK, starting from a CSV
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.
PDF AbstractCode
Datasets
DBpedia
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Cell Entity Annotation | BiodivTab | MAGIC | F1 (%) | 10 | # 5 | |
| Column Type Annotation | BiodivTab | MAGIC | F1 (%) | 14.2 | # 5 | |
| Cell Entity Annotation | ToughTables-DBP | MAGIC | F1 (%) | 18.4 | # 5 | |
| Column Type Annotation | ToughTables-DBP | MAGIC | F1 (%) | 15.9 | # 6 |
Methods
No methods listed for this paper. Add
relevant methods here
