Distributional negative sampling, which selects corrupted entities by cosine similarity to the original entity, improves several knowledge-base completion metrics for TransE and RESCAL on three benchmarks, though not universally.
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
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abstract
Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.
fields
cs.LG 1years
2019 1verdicts
REJECT 1representative citing papers
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Distributional Negative Sampling for Knowledge Base Completion
Distributional negative sampling, which selects corrupted entities by cosine similarity to the original entity, improves several knowledge-base completion metrics for TransE and RESCAL on three benchmarks, though not universally.