Nickel, M., & Kiela, D. (2017). Poincaré embeddings for learning hierarchical representations. In Advances in neural information processing systems (pp. 6338-6347).

University of Michigan - Ann Arbor

- The embedding models using the Euclidean vector space are unable to capture hierarchical relationships, which is not optimized for embedding structures like graphs and trees. Nickel and Kiela (2017) proposed a model that embeds concepts into a hyperbolic space, which can be thought of as a continuous version of trees structure and therefore provides a better fit to embed data with latent hierarchies.
- Poincaré ball model represents the entire hyperbolic space within the border of an n-dimensional sphere. This model, referred to as Poincaré Embeddings, generates the embeddings having as its input a weighted directed graph $G = \{(b, a)\}$, in which each node represents a concept and a directed edge between a and b means that a is a hypernym of b, in other words, b is a prerequisite concept of a. 

Improved Concept Embeddings for Learning Prerequisite Chains: Poincaré embeddings

References for Poincaré embeddings

The distance between two vectors $u$ and $v$ in the Poincaré sphere is
$$d(u, v) = cosh^{-1}(1+2\frac{(||u-v||)}{(1-||u||^2)(1-||v||^2)})$$
, all the vectors are within a distance of 1 from the origin.

Improved Concept Embeddings for Learning Prerequisite Chains: Poincaré space distance

The loss function used in this article is $\mathcal{L}(Θ, d)$, where embeddings $Θ = \{\theta_i\}_{i=1}^n$ and d is the Poincaré distance, that minimizes the Poincaré distance between similar concepts, in the meantime, maximizes the distance between different concepts.

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