Publication Link

https://hal.science/hal-03848124/document

Reference Implementation

https://github.com/alexis-cvetkov/KEN

Additional Implementations

No response

Relevance

KEN (Knowledge Embedding with Numbers) extends KG embedding models to numerical triples, e.g. (Paris, HasPopulation, 2.2M). The idea is the following: a numerical value $x$ from an attribute/relation $r$ is embedded into the same space as other entities: $e_r(x) = ReLU(x * w_r b_r) \in \mathbb{R}^p$.

Before embedding them, we normalize the values $x$ between 0 and 1, using a quantile transform.

We compared this model to LiteralE, and it performed better. Although we evaluated it on downstream tasks (external to the KG), and not on link prediction. We also did an ablation study to show the importance of the quantile transform and the ReLU activation.

Additional Context

I am the author of the model and have implemented the approach on top of PyKEEN. Now I would like to add it to the core package.