Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entity Embeddings with Conceptual Subspaces as a Basis for Plausible
  Reasoning   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.

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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
3
The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph