Conceptual spaces are geometric representations of conceptual knowledge, in
which entities correspond to points, natural properties correspond to convex
regions, and the dimensions of the space correspond to salient features. While
conceptual spaces enable elegant models of various cognitive phenomena, the
lack of automated methods for constructing such representations have so far
limited their application in artificial intelligence. To address this issue, we
propose a method which learns a vector-space embedding of entities from
Wikipedia and constrains this embedding such that entities of the same semantic
type are located in some lower-dimensional subspace. We experimentally
demonstrate the usefulness of these subspaces as (approximate) conceptual space
representations by showing, among others, that important features can be
modelled as directions and that natural properties tend to correspond to convex
regions.

本研究提出一种基于向量空间嵌入的方法，通过 Wikipedia 学习实体的嵌入并将其限制在某些较低维度子空间中，以解决概念空间表示建模自动化的局限性，实验结果表明这些子空间能够作为近似的概念空间表示，其重要特征可以被建模为方向，而自然属性则往往对应于凸区域。