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Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
2021
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
unpublished
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
doi:10.24963/ijcai.2021/395
fatcat:a54oy67webf2fa2cn3sqevsbwu