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Link Prediction using Numerical Weights for Knowledge Graph Completion within the Scholarly Domain

2021-01-31T12:14:11+01:00

Abstract Knowledge graphs (KGs) are widely used for modeling scholarly communication, informing scientometric analysis, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from [...]

Link Prediction using Numerical Weights for Knowledge Graph Completion within the Scholarly Domain2021-01-31T12:14:11+01:00

5* Knowledge Graph Embeddings with Projective Transformations

2021-01-31T12:17:54+01:00

Abstract Performing link prediction using knowledge graph embedding (KGE) models is a popular approach for knowledge graph completion. Such link predictions are performed by measuring the likelihood of links in the graph via a [...]

5* Knowledge Graph Embeddings with Projective Transformations2021-01-31T12:17:54+01:00

Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function

2021-01-31T12:18:45+01:00

Abstract Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, [...]

Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function2021-01-31T12:18:45+01:00
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