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Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world.
Instantiated rules contain constants extracted from KGs.
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph.
In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings.
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations.
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
#20 best model for Link Prediction on WN18RR