MMKG: Multi-Modal Knowledge Graphs
About
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.
Ye Liu, Hui Li, Alberto Garcia-Duran, Mathias Niepert, Daniel Onoro-Rubio, David S. Rosenblum• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Entity Alignment | FB15K-DB15K 50% (train) | Hits@146.4 | 24 | |
| Entity Alignment | FB15K-YAGO15K (50% train) | Hits@10.347 | 24 | |
| Entity Alignment | FB15K-DB15K (20% train) | Hits@10.126 | 18 | |
| Entity Alignment | FB15K-DB15K cross-KG (test) | H@166.6 | 15 | |
| Entity Alignment | FB15K-YAGO15K cross-KG (test) | H@157.3 | 15 | |
| Entity Alignment | FB15K-DB15K 80% seeds | Hits@10.666 | 14 | |
| Entity Alignment | FB15K-YAGO15K 20% seeds | H@10.113 | 14 | |
| Entity Alignment | FB15K-YAGO15K 80% seeds | H@10.573 | 14 |
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