Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
  Numeric values associated to edges of a knowledge graph have been used to
represent uncertainty, edge importance, and even out-of-band knowledge in a
growing number of scenarios, ranging from genetic data to social networks.
Nevertheless, traditional knowledge graph embedding models are not designed to
capture such information, to the detriment of predictive power. We propose a
novel method that injects numeric edge attributes into the scoring layer of a
traditional knowledge graph embedding architecture. Experiments with publicly
available numeric-enriched knowledge graphs show that our method outperforms
traditional numeric-unaware baselines as well as the recent UKGE model.
