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Compute association strength over semantic networks in a dimensionality-reduced form.

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assoc-space

Compute association strength over semantic networks in a dimensionality-reduced form.

This code is used for doing cool things with ConceptNet. It's probably useful for other things too.

The high-level idea is:

  • You have a relatively unstructured semantic network, with undirected edges
  • You want to find out how strongly connected two nodes of the network are (based on how many paths get you from node 1 to node 2, vs. how complex those paths are)
  • One way to do that is to apply spreading activation from node 1, and see how much of it reaches node 2, except that's terribly inefficient
  • Dimensionality reduction applies:
    • You could represent this semantic network as a sparse matrix A of which nodes are connected to which other nodes
    • You can represent a reasonable approximation to this semantic network as a smaller dense matrix U and a diagonal Σ, where U · Σ · U^T ~= A
    • (This is an application of SVD)
  • You can now simulate spreading activation with a really straightforward operation on Σ.
  • That's what assoc-space does.

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Compute association strength over semantic networks in a dimensionality-reduced form.

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