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Numeric values associated to edges of a knowledge graph have been used torepresent uncertainty, edge importance, and even out-of-band knowledge in agrowing number of scenarios, ranging from genetic data to social networks.Nevertheless, traditional knowledge graph embedding models are not designed tocapture such information, to the detriment of predictive power. We propose anovel method that injects numeric edge attributes into the scoring layer of atraditional knowledge graph embedding architecture. Experiments with publiclyavailable numeric-enriched knowledge graphs show that our method outperformstraditional numeric-unaware baselines as well as the recent UKGE model.

Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes