Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Table 2: Number of dimensions selected by the nuclear norm (NN) regularization component of our model for some of the semantic types.
['Semantic Type', 'Number of Entities', 'NN-Dimensions']
An important aspect in the discussion of the result is to analyze the effectiveness of nuclear norm regularization in identifying the most appropriate number of dimensions for each of the semantic types. As expected, semantic types with more entities generally end up being associated with higher-dimensional subspaces, but other factors affect the choice as well. For example, note that house has fewer instances than railway station, while being represented by a higher-dimensional subspace. Intuitively, this reflects the idea that the type house is more diverse or complex than the type railway station.