Papers by Yasmin Salehi

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Challenge:	Existing methods for learning low-dimensional representations of entities and relations in knowledge graphs employing corruption distributions that generate hard negative samples.
Approach:	They propose a structure-aware negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node’s k-hop neighborhood.
Outcome:	The proposed method finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization.