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Found 6 papers, 5 papers with code
Do Similar Entities have Similar Embeddings?
A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP).
PyGraft: Configurable Generation of Synthetic 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 which demonstrates both the generality and superiority of our proposed approach.
Sem@$K$: Is my knowledge graph embedding model semantic-aware?
That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w. r. t.
