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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

TaskDatasetResultRank
Entity AlignmentFB15K-DB15K 50% (train)
Hits@146.4
24
Entity AlignmentFB15K-YAGO15K (50% train)
Hits@10.347
24
Entity AlignmentFB15K-DB15K (20% train)
Hits@10.126
18
Entity AlignmentFB15K-DB15K cross-KG (test)
H@166.6
15
Entity AlignmentFB15K-YAGO15K cross-KG (test)
H@157.3
15
Entity AlignmentFB15K-DB15K 80% seeds
Hits@10.666
14
Entity AlignmentFB15K-YAGO15K 20% seeds
H@10.113
14
Entity AlignmentFB15K-YAGO15K 80% seeds
H@10.573
14
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