Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling

  title={Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling},
  author={Vibhor Kanojia and Hideyuki Maeda and Riku Togashi and Sumio Fujita},
  journal={Proceedings of the 26th International Conference on World Wide Web Companion},
Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE[1], TransH[2], and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate… 

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