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Found 4 papers, 4 papers with code
PyGraft: Configurable Generation of Schemas and Knowledge Graphs at Your Fingertips
In some data-sensitive fields such as education or medicine, access to public datasets is even more limited.
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
These models learn a vector representation of knowledge graph entities and relations, a. k. a.
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach.
Sem@$K$: Is my knowledge graph embedding model semantic-aware?
Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities.
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